Abstract. This paper addresses the issue of identifying persons with expertise knowledge on a given topic. Traditional methods usually estimate the relevance between the query and the support documents of candidate experts using, for example, a language model. However, the language model lacks the ability of identifying semantic knowledge, thus results in some right experts cannot be found due to not occurrence of the query terms in the support documents. In this paper, we propose a mixture model based on Probabilistic Latent Semantic Analysis (PLSA) to estimate a hidden semantic theme layer between the terms and the support documents. The hidden themes are used to capture the semantic relevance between the query and the experts. We evaluate our mixture model in a real-world system, ArnetMiner 1. Experimental results indicate that the proposed model outperforms the language models. 1

Abstract: The amount of information on the web is permanently growing. The orientation within the information is becoming more and more complicated. It is necessary to develop a system able to adapt the information for the needs of the user and to personalize the presentation of information according to his/her preferences. In this paper we discuss current approaches used in the research, both advantages and disadvantages of the developed models and our proposals for their extensions. Key words: meta-model, web adaptation, web personalization

Search Engines today often return a large volume of results with possibly a few relevant results. The notion of relevance is subjective and depends on the user and the context of search. Re-ranking of these results to reflect the most relevant results to the user, using a user profile built from the relevance feedback has proved to provide good results. Our approach assumes implicit feedback gathered from a search engine query logs and learn a user profile. The user profile typically runs into sparsity problems due to the sheer volume of the WWW. Sparsity refers to the missing weights of certain words in the user profile. In this paper we present an effective re-ranking strategy that compensates for the sparsity in a user’s profile, by applying collaborative filtering algorithms. Our evaluation results show an improvement in precision over approaches that use only a user’s profile.

It is widely assumed that exploiting multiple sources of evidence in information retrieval approaches allows improving the search accuracy. For instance personalized information retrieval exploits evidence issued from the user profile elements like interests, preferences and tasks in the information retrieval process in order to better fit the user’s expectations. Recent studies exploit another evidence like the user intent behind the query, classified as informational, navigational or transactional in order to carry out a specific search. However, the strategies involved focus solely on exploiting document features to leverage the relevance estimation according to the user intent category. In this paper, we show how to incorporate both user intent and user profile evidences, in the same framework, for personalizing the informational search. Our framework description includes user intent prediction based on contextual query classification method, and user profiling based on modeling the user interests. Query classification relies on using both query features and the current query session category called the query profile. Then, personalizing informational search emphasizes the user profile in a personalized document ranking. Preliminary experimental results were carried out using TREC data collection and show that our approach is promising.

Retrieval tasks typically require a ranking of items given a query. Collaborative filtering tasks, on the other hand, learn to model user’s preferences over items. In this paper we study the joint problem of recommending items to a user with respect to a given query, which is a surprisingly common task. This setup differs from the standard collaborative filtering one in that we are given a query × user × item tensor for training instead of the more traditional user × item matrix. Compared to document retrieval we do have a query, but we may or may not have content features (we will consider both cases) and we can also take account of the user’s profile. We introduce a factorized model for this new task that optimizes the top-ranked items returned for the given query and user. We report empirical results where it outperforms several baselines. 1.