Hi, I'm Sarah PhD in Computer Engineering. My research is about group based personalization using topical user profile.
In this talk, I'd like to start
with an introduction to personalization and especially personalization, using user
profile, personalization, using user profiles is investigated in lots
of research in recent years. Personalization is the task of re-ranking
the retrieved documents set concerning the user profile. Actually,
personalization uses re-ranking algorithms to improve the returned result by search
engine Although user profiles are indicatives of the user interest.
But they have the limitation for new users or users without any history and in more
times it is needed to use a group of user profile to re-rank the returned Result
by search engine. A cluster of users in the form of a group profile can be used as
a solution to the sparsity problem. One of the disadvantage of the personalization
based user profile is that it is built by considering only the documents that
the users has clicked. The set of the clicked document might be sparse for some users.
To overcome the problem in this research, we present a group based personalization
model using typical user profile and comparing the result of the proposed
ranking methods based on the group and user profile. To cluster the groups
of users, we use the kmeans clustering algorithm and the similarity between users
is measured by the symmetric kullback-leibler divergence between theire latent in topic
distribution. In the issues related to privacy concerns group profiles are also
more secure than user profile because both the computation and their storage
of the user information are done as a group of users. So let's have a look
at the user profiling model. In this model we have users and the clicked documents
for users. Using these documents we have tried to create topical user profile.
We've used LDA Latent Drichlet Allocation model to create topical user profiles.
And the probability of the topic for the given document is obtained from LDA topic
model directly. To measure the similarity between users, we need to create user
profiles. The user topical profile of a user is defined as a vector of
the obtained topics of the LDA model on the clicked documents as probability of user given topic z.
We can find the similarity between users from a symmetric between any
pairs of users from symmetric kullback-labeler divergence between theire latent
topic distribution as in Equation one. The kullback-Laibler divergence between theire
latent topic distributions can be measured using equation three and four. As the KL is not
symmetric algorithm, we measure the sum between KLs. This transformation ensures
that we get a similarity value within the interval zero and one. Actually user
profiles are incomplete for new or unseen users. To resolve the data
sparsity, the history of similar users can be grouped and used
for personalization. In continue we represent a group based personalization
by changing in the re-ranking algorithm for user profiles.
As previously mentioned, a user topical profile is modeled by the set
of documents which the user clicked on. Then users are clustered using
the similarity between theire topic probability distribution or probability z
given user. K-means clustering algorithm is used to partition the users
to C groups and the number of clusters is a parameter and Cu
is the cluster of user u. We model the group based personalization
in equation five and six. In this equation, the ranking score or probability d given query
and Cu is produced using probability of document, probability
of words in query and topics in documents. Equation 6 generalizes the user
ranking to the cluster of users, resolving the sparsity problem. The probability
of Cu given z is estimated using equation 7 and the lambda parameter
weights the effect of group profiles on the ranking process and group profiles
are computed as follows. Similar to conducted work in twenty five
an exponentially decaying function is used in the user profile to give more weight
to recently clicked documents. Considering Cu equal one, this equation could be
calculated for a group of users. In the evaluation part, the first we discuss
about data set. For the experiment AOL TREC 2013 session Track
and TREC 2014 session track of web search engine logs are used. AOL query
log containing three months query log in 2006 is used in this research
and we cleaned the data set by only retaining queries which resulted
in a click on a URL. In other part of research, we used the session track consist
of query sessions for different topics along with the clicked documents and user.
To evaluate the personalization using group profiles, we divided the dataset into
ninty five percent for training and the last five percent of queries
for testing. The personalization metrics are success at rank k, s@k,
the mean reciprocal rank MRR up to rank rank 10 and normalized discounted
cumulative gain. The number of topics used for LDA is an important parameter.
The relationship between MRR and this parameter is investigated in a small
development. The result indicates that using 40, 30 and 30,
topics yields the best results in the AOL ,session tracks 2013 and session
track 2014. To investigate the relationship between the number
of clusters and MRR, different number of clusters are evaluated and as shown,
in figure 2, the best result is obtained with k=30, k=20 and k=20,
for AOL, TREC 2013 and 2014 datasets. To test our hypothesis that
group profiles resolve, the sparsity problem, we compare the personalization Effective
of effectiveness of user and group based ranking methods. Three ranking methods are used
in our evaluation: genetic ranking without personalization, user profiles based
personalization and group profile based personalization. To compare
the personalization methods with Baseline methods which no personalization,
we used TFIDF and BM25 a probabilistic weighting as baseline
models. To calculate the baseline, we ranked each result using
the tfidf and BM25 scoring as long term with no user
profile. Table 1,2 and 3 report the MRR, s@1,
and s@10 scores for the four methods. TfIDF and BM25
personalized twenty five personalized topic Model using a user that
we called here UTM user topic model and personalized topic model using a group
of users called GTM Group Topic model here in AOL, session Track datasets.
In all tables, the first and second rows represent the ranking scores when using
the TFIDF and BM25 methods. The third method is related
to the personalized ranking model using user profiles. Finally, the last method
shows the result when all queries are re-ranked, using group-based profiles.
In all tables, we experimented the results for short, session and long term.
Short term is included one data of one month, while session is for 1 session
and long consists of all data. As shown in tables 1,2 and 3 the ranking results
of TFIDF and BM25 with no personalization are less than the other
personalization methods. When we consider the result of group-based
personalization using the GTM model, It is evidence that it achieves a higher score
for all short, session and long term profiles in all datasets. This indicates
that using group based personalization is a better strategy than using user based
personalization in all short, session and long term. Also, the short term profiles
show higher MRR than session and long term profiles in all datasets. For further
research, we investigated two models, UTM and GTM using nDCG
metric in track datasets using provided judgment in these data sets. Figures 3
and 4 show the nDCG@10 based performance comparison for the two
personalisation using user and group in session track datasets. As can be observed using
group profiles improves nDCG@10 by an average of four percent in two datasets
when using long term user profile, while short term and session term user profiles
are improved by five percent and six percent accordingly. So using group profile
instead of user profile improves all cases. As expected the short term user profile
is more effective than the others. As a conclusion in this article, a new group
based personalization using topical user profiles is proposed for sparsity problem
in personalization of new or without history users. Our proposed model
for personalization is defined in terms of the latent topic models for group
of users, rather than relying solely on the user history directly.
And the results show the improvements about 7 and 6 percent in short
and long term profiles. Thank you for your attention and if you have any
question, please feel free to contact me.