TEMPER : A Temporal Relevance Feedback Method

 
TEMPER : A Temporal Relevance Feedback
                   Method

              Mostafa Keikha, Shima Gerani and Fabio Crestani
         {mostafa.keikha, shima.gerani, fabio.crestani}@usi.ch

                       University of Lugano, Lugano, Switzerland

        Abstract. The goal of a blog distillation (blog feed search) method is to
        rank blogs according to their recurrent relevance to the query. An inter-
        esting property of blog distillation which differentiates it from traditional
        retrieval tasks is its dependency on time. In this paper we investigate the
        effect of time dependency in query expansion. We propose a framework,
        TEMPER, which selects different terms for different times and ranks
        blogs according to their relevancy to the query over time. By generat-
        ing multiple expanded queries based on time, we are able to capture the
        dynamics of the topic both in aspects and vocabulary usage. We show
        performance gains over the baseline techniques which generate a single
        expanded query using the top retrieved posts or blogs irrespective of
        time.

1     Introduction
User generated content is growing very fast and becoming one of the most im-
portant sources of information on the Web. Blogs are one of the main sources
of information in this category. Millions of people write about their experiences
and express their opinions in blogs everyday.
    Considering this huge amount of user generated data and its specific prop-
erties, designing new retrieval methods is necessary to facilitate addressing dif-
ferent types of information needs that blog users may have. Users’ information
needs in blogosphere are different from those of general Web users. Mishne and
de Rijke [1] analyzed a blog query log and accordingly they divided blog queries
into two broad categories called context and concept queries. In context queries
users are looking for contexts of blogs in which a Named Entity occurred to find
out what bloggers say about it, whereas in concept queries they are looking for
blogs which deal with one of searcher’s topics of interest. In this paper we focus
on the blog distillation task (also known as blog feed search)1 where the goal is
to answer topics from the second category [2].
    Blog distillation is concerned with ranking blogs according to their recurring
central interest to the topic of a user’s query. In other words, our aim is to
discover relevant blogs for each topic2 that a user can add to his reader and read
them in future [3].
1
    In this paper we use words “feed” and “blog” interchangeably
2
    In this paper we use words “topic” and “query” interchangeably
An important aspect of blog distillation, which differentiates it from other
IR tasks, is related to the temporal properties of blogs and topics. Distillation
topics are often multifaceted and can be discussed from different perspectives
[4]. Vocabulary usage in the relevant documents to a topic can change over time
in order to express different aspects (or sub-topics) of the query. These dynamics
might create term mismatch problem during the time, such that a query term
may not be a good indicator of the query topic in all different time intervals. In
order to address this problem, we propose a time-based query expansion method
which expands queries with different terms at different times. This contrasts
other applied query expansion methods in blog search where they generate only
one single query in the expansion phase [5, 4]. Our experiments on different
test collections and different baseline methods indicate that time-base query
expansion is effective in improving the retrieval performance and can outperform
existing techniques.
     The rest of the paper is organized as follows. In section 2 we review state of
the art methods in blog retrieval. Section 3 describes existing query expansion
methods for blog retrieval in more detail. Section 4 explains our time-based
query expansion approach. Experimental results over different blog data sets are
discussed in section 6. Finally, we conclude the paper and describe future work
in section 7.

2   Related Work

The main research on the blog distillation started after 2007, when the TREC
organizers proposed this task in the blog track [3]. Researchers have applied
different methods from areas that are similar to blog distillation, like ad-hoc
search, expert search and resource selection in distributed information retrieval.
    The most simple models use ad-hoc search methods for finding relevant blogs
to a specific topic. They treat each blog as one long document created by con-
catenating all of its posts together [6, 4, 7]. These methods ignore any specific
property of blogs and mostly use standard IR techniques to rank blogs. Despite
their simplicity, these methods perform fairly well in blog retrieval.
    Some other approaches have been applied from expert search methods in
blog retrieval [8, 2]. In these models, each post in a blog is seen as evidence that
the blog has an interest in the query topic. In [2], MacDonald et al. use data
fusion models to combine this evidence and compute a final relevance score for
the blog, while Balog et al. adapt two language modeling approaches of expert
finding and show their effectiveness in blog distillation [8].
    Resource selection methods from distributed information retrieval have been
also applied to blog retrieval [4, 9, 7]. Elsas et al. deal with blog distillation
as a recourse selection problem [4, 9]. They model each blog as a collection of
posts and use a Language Modeling approach to select the best collection. A
similar approach is proposed by Seo and Croft [7], which they call Pseudo Cluster
Selection. They create topic-based clusters of posts in each blog and select blogs
that have the most similar clusters to the query.
Temporal properties of posts have been considered in different ways in blog
retrieval. Nunes et al. define two new measures called “temporal span” and “tem-
poral dispersion” to evaluate “how long” and “how frequently” a blog has been
writing about a topic [10]. Similarly Macdonald and Ounis [2] use a heuristic
measure to capture the recurring interests of blogs over time. Some other ap-
proaches give higher scores to more recent posts before aggregating them [11,
12]. All these proposed methods and their improvements show the importance
and usefulness of temporal information in blog retrieval. However, none of the
mentioned methods investigates the effect of time on the vocabulary change for
a topic. We employ the temporal information as a source to distinguish between
different aspects of topic and terms that are used for each aspect. This leads us
to a time-based query expansion method where we generate mutliple expanded
queries to cover multiple aspects of a topic over time.
    Different query expansion possibilities for blog retrieval have been explored
by Elsas et al. [4] and Lee et al. [5]. Since we use these methods as our baselines,
we will discuss them in more detail in the next section.

3   Query Expansion in Blog Retrieval

Query expansion is known to be effective in improving the performance of the
retrieval systems [13–15]. In general the idea is to add more terms to an initial
query in order to disambiguate the query and solve the possible term mismatch
problem between the query and the relevant documents. Automatic Query Ex-
pansion techniques usually assume that top retrieved documents are relevant to
the topic and use their content to generate an expanded query. In some situa-
tions, it has been shown that it is better to have multiple expanded queries as
apposed to the usual single query expansion, for example in server-based query
expansion technique in distributed information retrieval [16].
    An expanded query, while being relevant to the original query, should have
as much coverage as possible on all aspects of the query. If the expanded query is
very specific to some aspect of the original query, we will miss part of the relevant
documents in the re-ranking phase. In blog search context, where queries are
more general than normal web search queries [4], the coverage of the expanded
query gets even more important. Thus in this condition, it might be better to
have multiple queries where each one covers different aspects of a general query.
    Elsas et al. made the first investigation on the query expansion techniques
for blog search [4]. They show that normal feedback methods (selecting the new
terms from top retrieved posts or top retrieved blogs) using the usual parameter
settings is not effective in blog retrieval. However, they show that expanding
query using an external resource like Wikipedia can improve the performance of
the system. In a more recent work, Lee et al. [5] propose new methods for select-
ing appropriate posts as the source of expansion and show that these methods
can be effective in retrieval. All these proposed methods can be summarized as
follows:
– Top Feeds: Uses all the posts of the top retrieved feeds for the query expan-
   sion. This model has two parameters including number of selected feeds and
   number of the terms in the expanded query [4].
 – Top Posts: Uses the top retrieved posts for the query expansion. Number
   of the selected posts and number of the terms to use for expansion are the
   parameters of this model [4].
 – FFBS: Uses the top posts in the top retrieved feeds as the source for selecting
   the new terms. Number of the selected posts from each feed is fixed among
   different feeds. This model has three parameters; number of the selected
   feeds, number of the selected posts in each feed and number of the selected
   terms for the expansion [5].
 – WFBS: Works the same as FFBS. The only difference is that number of the
   selected posts for each feed depends on the feed rank in the initial list, such
   that more relevant feeds contribute more in generating the new query. Like
   FFBS, WFBS has also three parameters that are number of the selected
   feeds, total number of the posts to be used in the expansion and number of
   the selected terms [5].

    Among the mentioned methods,“Top Feeds” method has the possibility to
expand the query with non-relevant terms. The reason is that all the posts
in a top retrieved feed are not necessarily relevant to the topic. On the other
hand, “Top Posts” method might not have enough coverage on all the sub-
topics of the query, because the top retrieved posts might be mainly relevant to
some dominant aspect of the query. FFBS and WFBS methods were originally
proposed in order to have more coverage than the “Top Posts” method while
selecting more relevant terms than the “Top Feeds” method [5]. However, since
it is difficult to summarize all the aspects of the topic in one single expanded
query, these methods would not have the maximum possible coverage.

4   TEMPER

In this section we describe our novel framework for time-based relevance feedback
in blog distillation called TEMPER. TEMPER assumes that posts at different
times talk about different aspects (sub-topics) of a general topic. Therefore,
vocabulary usage for the topic is time-dependant and this dependancy can be
considered in a relevance feedback method. Following this intuition, TEMPER
selects time-dependent terms for query expansion and generated one query for
each time point. We can summarize the TEMPER framework in the following 3
steps:

1. Time-based representation of blogs and queries
2. Time-based similarity between a blogs and a query
3. Ranking blogs according to the their overall similarity to the query.

    In the remainder of this section, we describe our approach in fulfilling each
of these steps.
4.1   Time-Based Representation of Blogs and Queries

Initial Representation of Blogs and Queries In order to consider time in
the TEMPER framework, we first need to represent blogs and queries in the
time space.
    For a blog representation, we distribute its posts based on their publish date.
In order to have a daily representation of the blog, we concatenate all the posts
that have the same date.
    For a query representation, we take advantage of the top retrieved posts for
the query. Same as blog representation, we select the top K relevant posts for
the query and divide them based on their publish date while concatenating posts
with the same date. In order to have a more informative representation of the
query, we select the top N terms for each day using the KL-divergence between
the term distribution of the day and the whole collection [17].
    Note that in the initial representation, there can be days that do not have
any term distribution associated with them. However, in order to calculate the
relevance of a blog to a query, TEMPER needs to have the representation of the
blog and query in all the days. We employ the available information in the initial
representation to estimate the term distributions for the rest of the days. In the
rest of this section, we explain our method for estimating these representations.

Term Distributions Over Time TEMPER generates a representation for
each topic or blog for each day based on the idea that a term at each time posi-
tion propagates its count to the other time positions through a proximity-based
density function. By doing so, we can have a virtual document for a blog/topic
at each specific time position. The term frequencies of such a document is cal-
culated as follows:
                                        T
                                        X
                           0
                         tf (t, d, i) =   tf (t, d, j)K(i, j)               (1)
                                     j=1

where i and j indicate time position (day) in the time space. T denotes the
time span of the collection. tf 0 shows the term frequency of term t in blog/topic
d at day i and it is calculated based on the frequency of t in all days. K(i, j)
decreases as the distance between i and j increases and can be calculated using
kernel functions that we describe later.
   The proposed representation of document in the time space is similar to
the proximity-based method where they generate a virtual document at each
position of the document in order to capture the proximity of the words [18, 19].
However, here we aim to capture the temporal proximity of terms. In this paper
we employ the laplace kernel function which has been shown to be effective in
a previous work [19] together with the Rectangular (square) kernel function.
In the following formulas, we present normalized kernel functions with their
corresponding variance formula.
1. Laplace Kernel
                                                         
                                       1        − |i − j|
                           k(i, j) =      exp
                                       2b           b                          (2)
                                           where σ 2 = 2b2

2. Rectangular Kernel
                                            1
                                       
                                           2a   if |i − j| ≤ a
                           k(i, j) =
                                           0      otherwise
                                                                               (3)
                                                  a2    2
                                        where σ =
                                                  3

4.2   Time-Based Similarity Measure
By having the daily representation of queries and blogs, we can calculate the
daily similarity between these two representations and create a daily similarity
vector for the blog and the query. The final similarity between the blog and the
query is then calculated by summing over the daily similarities:
                                                  T
                                                  X
                      simtemporal (B, Q) =              sim(B, Q, i)           (4)
                                                  i=1

where sim(Bi , Qi ) shows the similarity between a blog and a query representa-
tion at day i and T shows the time span of the collection in days.
    Another popular method in time series similarity calculation is to see each
time point as one dimension in the time space and use the euclidian length of
the daily similarity vector as the final similarity between the two representations
[20]:                                       v
                                            u T
                                            uX
                     simtemporal (B, Q) = t        sim(B, Q, i)2                (5)
                                                   i=1

    We use the cosine similarity as a simple and effective similarity measure
for calculating similarity between the blog and the topic representations at the
specific day i:
                                  P
                                      tf (w, B, i) × tf (w, Q, i)
               sim(B, Q, i) = pP w                  P                        (6)
                                                2                    2
                                  w tf (w, B, i) ×     w tf (w, Q, i)

   The normalized value of the temporal similarity over all blogs is then used
as Ptemporal .
                                     simtemporal (B, Q)
                Ptemporal (B|Q) = P                   0
                                                                           (7)
                                    B 0 simtemporal (B , Q)
    Finally in order to take advantage of all the available evidence regarding
the blog relevance, we interpolate the temporal score of the blog with its initial
relevance score.
Table 1. Effect of cleaning the data set on Blogger Model. Statistically significant
improvements at the 0.05 level is indicated by †.

                        Model   Cleaned MAP P@10 Bpref
                     BloggrModel No     0.2432 0.3513 0.2620
                     BloggrModel Yes 0.2774† 0.4154 † 0.2906†

                 P (B|Q) = αPinitial (B|Q) + (1 − α)Ptemporal (B|Q)                   (8)

where α is a parameter that controls the amount of temporal relevance that
is considered in the model. We use the Blogger Model method for the initial
ranking of the blogs [8] . The only difference with the original Blogger Model is
that we set the prior of a blog to be proportional to the log of the number of
its posts, as opposed to the uniform prior that was used in the original Blogger
Model. This log-based prior has been used and shown to be effective by Elsas et
al. [4].

5     Experimental Setup
In this section we first explain our experimental setup for evaluating the effec-
tiveness of the proposed framework.

Collection and Topics We conduct our experiments over three years worth
of TREC blog track data from the blog distillation task, including TREC’07,
TREC’08 and TREC’09 data sets. The TREC’07 and TREC’08 data sets include
45 and 50 assessed queries respectively and use Blog06 collection. The TREC’09
data set uses Blog08, a new collection of blogs, and has 39 new queries 3 We use
only the title of the topics as the queries.
    The Blogs06 collection is a crawl of about one hundred thousand blogs over an
11-weeks period [22], and includes blog posts (permalinks), feed, and homepage
for each blog. Blog08 is a collection of about one million blogs crawled over a year
with the same structure as Blog06 collection [21]. In our experiments we only
use the permalinks component of the collection, which consist of approximately
3.2 million documents for Blog06 and about 28.4 million documents for Blog08.
    We use the Terrier Information Retrieval system4 to index the collection with
the default stemming and stopwords removal. The Language Modeling approach
using the dirichlet-smoothing has been used to score the posts and retrieve top
posts for each query.
3
    Initially there were 50 queries in TREC 2009 data set but some of them did not have
    relevant blogs for the selected facets and are removed in the official query set [21].
    We do not use of the facets in this paper however we use the official query set to be
    able to compare with the TREC results.
4
    http://ir.dcs.gla.ac.uk/terrier/
Table 2. Evaluation results for the implemented models over TREC09 data set.

                 Model                   MAP              P@10     Bpref
              BloggerModel              0.2774           0.4154   0.2906
                TopFeeds                0.2735           0.3897   0.2848
                TopPosts                0.2892           0.4230   0.3057
                  FFBS                  0.2848           0.4128   0.3009
                 WFBS                   0.2895           0.4077   0.3032
          TEMPER-Rectangular-Sum        0.2967   †       0.4128   0.3116 †
        TEMPER-Rectangular-Euclidian    0.3014   †   ‡ ∗ 0.4435 ∗ 0.3203 † ‡ ∗
           TEMPER-Laplace-Sum           0.3086   †       0.4256   0.3295 †
         TEMPER-Laplace-Euclidian       0.3122   †   ‡ ∗ 0.4307   0.3281 † ∗

Retrieval Baselines We perform our feedback methods on the results of the
Blogger Model method [8]. Therefore, Blogger Model is the first baseline against
which, we will compare the performance of our proposed methods. The second
set of baselines are the query expansion methods proposed in previous works
[4, 5]. In order to have a fair comparison, we implemented the mentioned query
expansion methods on top of Blogger Model. We tuned the parameters of these
models using 10-fold cross validation in order to maximize MAP.
     The last set of baselines are provided by TREC organizers as part of the blog
facet distillation task. We use these baselines to see the effect of TEMPER in
re-ranking the results of other retrieval systems.

Evaluation We used the blog distillation relevance judgements provided by
TREC for evaluation. We report the Mean Average Precision (MAP) as well as
binary Preference (bPref), and Precision at 10 documents (P@10). Throughout
our experiments we use the Wilcoxon signed ranked matched pairs test with a
confidence level of 0.05 level for testing statistical significant improvements.

6     Experimental Results
In this section we explain the experiments that we conducted in order to eval-
uate the usefulness of the proposed method. We mainly focus on the results of
TREC09 data set, as it is the most recent data set and has enough temporal
information which is an important feature for our analysis. However, in order to
see the effect of the method on the smaller collections, we briefly report the final
results on the TREC07 and TREC08 data sets.
    Table 1 shows the evaluation results of Blogger Model on TREC09 data set.
Because of the blog data being highly noisy, we carry out a cleaning step on
the collection in order to improve the overall performance of the system. We use
the cleaning method proposed by Parapar et al. [23]. As we can see in Table
1, cleaning the collection is very useful and improves the MAP of the system
about 14%. We can see that the results of Blogger Model on the cleaned data is
already better than the best TREC09 submission on the title-only queries.
Table 3. Evaluation results for the implemented models over TREC08 data set.

                 Model                MAP            P@10           Bpref
              BloggerModel           0.2453         0.4040         0.2979
                TopPosts             0.2567         0.4080         0.3090
                 WFBS                0.2546         0.3860         0.3087
          TEMPER-Laplace-Euclidian   0.2727 † ‡ ∗   0.4380 † ‡ ∗   0.3302 † ∗

    Table 4. Evaluation results for the implemented models over TREC07 data set.

                     Model                 MAP      P@10       Bpref
                  BloggerModel            0.3354   0.4956     0.3818
                    TopPosts              0.3524 † 0.5044     0.3910
                     WFBS                 0.3542 † 0.5356 † ‡ 0.3980
              TEMPER-Laplace-Euclidian    0.3562 † 0.5111     0.4011

    Table 2 summarizes retrieval performance of Blogger Model and the baseline
query expansion methods along with different settings of TEMPER on the TREC
2009 data set. The best value in each column is bold face. A dag(†), a ddag(‡)
and a star(∗) indicate statistically significant improvement over Blogger Model,
TopPosts and WFBS respectively. As can be seen from the table, none of the
query expansion baselines improves the underlying Blogger Model significantly.
    From table 2 we can see that TEMPER with different settings (using rectan-
gular/laplace kernel, sum/euclidean similarity method) improves Blogger Model
and the query expansion methods significantly. These results show the effective-
ness of time-based representation of blogs and query and highlights the impor-
tance of time-based similarity calculation of blogs and topics.
    In tables 3 and 4 we present similar results over TREC08 and TREC07
data sets. Over the TREC08 dataset, it can be seen that TEMPER improves
Blogger Model and different query expansion methods significantly. Over the
TREC07 dataset, TEMPER improves Blogger Model significantly. However, the
performance of TEMPER is comparable with the other query expansion methods
and the difference is not statistically significant.
    As it was mentioned in section 5, we also consider the three standard base-
lines provided by TREC10 organizers in order to see the effect of our proposed
feedback method on retrieval baselines other than Blogger Model. Table 8 shows
the results of TEMPER over the TREC baselines. It can be seen that TEMPER
improves the baselines in most of the cases. The only baseline that TEMPER
does not improve significantly is stdbaseline15 .
    Tables 5, 6 and 7 show the performance of TEMPER compared to the best
title-only TREC runs in 2009, 2008 and 2007 respectively. It can be seen from
the tables that TEMPER is performing better than the best TREC runs over the
TREC09 dataset. The results over the TREC08 and TREC07 are comparable
5
    Note that the stdbaslines are used as blackbox and we are not yet aware of the
    underlying method
Table 5. Comparison with the best TREC09 title-only submissions.

                        Model                 MAP       P@10       Bpref
               TEMPER-Laplace-Euclidian      0.3122    0.4307     0.3281
              TREC09-rank1 (buptpris 2009)   0.2756    0.3206     0.2767
                TREC09-rank2 (ICTNET)         0.2399   0.3513     0.2384
                  TREC09-rank3 (USI)          0.2326   0.3308     0.2409

        Table 6. Comparison with the best TREC08 title-only submissions.

                        Model                   MAP      P@10      Bpref
               TEMPER-Laplace-Euclidian        0.2727   0.4380    0.3302
              TREC08-rank2 (CMU-LTI-DIR)       0.3056    0.4340   0.3535
                 TREC08-rank1 (KLE)            0.3015   0.4480    0.3580
                 TREC08-rank3 (UAms)           0.2638    0.4200   0.3024

to the best TREC runs and can be considered as the third and second best
reported results over TREC08 and TREC07 datasets respectively. TEMPER has
four parameters including : number of the posts selected for expansion, number
of the terms that are selected for each day, standard deviation (σ) of the kernel
functions and α as the weight of the initial ranking score.
    Among these parameters, we fix number of the terms for each day to be 50, as
used in a previous work [4]. Standard deviation of the kernel function is estimated
using top retrieve posts for each query. Since the goal of the kernel function is
to model the distribution of distance between two consequent relevant posts, we
assume the distances between selected posts (top retrieved posts) as the samples
of this distribution. We then use the standard deviation of the sample as an
estimation for σ.
    The other two parameters are tuned using 10-fold cross validation method.
Figure 1 and 2 show sensitivity of the system to these parameters. It can be
seen that the best performance is gained by selecting about 150 posts for ex-
pansion while any number more than 50 gives a reasonable result. The value
of α depends on the underneath retrieval model. We can see that TEMPER
outperforms Blogger Model for all values of α and the best value is about 0.1.

7   Conclusion and Future Work

In this paper we investigated blog distillation where the goal is to rank blogs
according to their recurrent relevance to the topic of the query. We focused on
the temporal properties of blogs and its application in query expansion for blog
retrieval. Following the intuition that term distribution for a topic might change
over time, we propose a time-based query expansion technique. We showed that
it is effective to have multiple expanded queries for different time points and score
the posts of each time using the corresponding expanded query. Our experiments
on different blog collections and different baseline methods showed that this
method can improve the state of the art query expansion techniques.
Table 7. Comparison with the best TREC07 title-only submissions.

                       Model                   MAP        P@10     Bpref
               TEMPER-Laplace-Euclidian       0.3562 †   0.5111   0.4011
                 TREC07-rank1 (CMU)           0.3695     0.5356   0.3861
               TREC07-rank2 (UGlasgow)         0.2923    0.5311   0.3210
                TREC07-rank3 (UMass)           0.2529    0.5111   0.2902

Table 8. Evaluation results for the standard baselines on TREC09 data set. Statisti-
cally significant improvements are indicated by †.

                        Model               MAP       P@10       Bpref
                     stdBaseline1          0.4066     0.5436    0.4150
                  TEMPER-stdBaseline1      0.4114     0.5359    0.4182
                     stdBaseline2          0.2739     0.4103    0.2845
                  TEMPER-stdBaseline2      0.3009†   0.4308 †   0.3158†
                     stdBaseline3          0.2057     0.3308    0.2259
                  TEMPER-stdBaseline3      0.2493†   0.4026†    0.2821†

    Future work will involve more analysis on temporal properties of blogs and
topics. In particular, modeling the evolution of topics over time can help us to
better estimate the topics relevance models. This modeling over time can be seen
as a temporal relevance model which is an unexplored problem in blog retrieval.
8    Acknowledgement
This work was supported by Swiss National Science Foundation (SNSF) as XMI
project (ProjectNr. 200021-117994/1).
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0.316
                                        TEMPER                                                     TEMPER
  0.314                                                       0.32                            Blogger Model

  0.312
      0.31                                                    0.31

  0.308
                                                               0.3
  0.306
MAP

                                                        MAP
  0.304
                                                              0.29
  0.302
       0.3                                                    0.28
  0.298
  0.296                                                       0.27
  0.294
             0   50       100     150       200   250                0   0.2   0.4      0.6          0.8      1
                      Number of the posts                                         Alpha

Fig. 1. Effect of number of the posts                   Fig. 2. Effect of alpha on the perfor-
used for expansion on the performance                   mance of TEMPER.
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