IRIT, GeoComp, and LIUPPA at the TREC 2013 Contextual Suggestion Track

International audienceIn this paper we give an overview of the participation of the IRIT, GeoComp, and LIUPPA labs in the TREC 2013 Contextual Suggestion Track. Our framework combines existing geo-tools or services (e.g., Google Places, Yahoo! BOSS Geo Services, PostGIS, Gisgraphy, GeoNames) and ranks results according to features such as context-place distance, place popularity, and user preferences. We participated in the Open Web and ClueWeb12 sub-tracks with runs IRIT.OpenWeb and IRIT.ClueWeb

INEX Tweet Contextualization Task: Evaluation, Results and Lesson Learned

Microblogging platforms such as Twitter are increasingly used for on-line client and market analysis. This motivated the proposal of a new track at CLEF INEX lab of Tweet Contextualization. The objective of this task was to help a user to understand a tweet by providing him with a short explanatory summary (500 words). This summary should be built automatically using resources like Wikipedia and generated by extracting relevant passages and aggregating them into a coherent summary. Running for four years, results show that the best systems combine NLP techniques with more traditional methods. More precisely the best performing systems combine passage retrieval, sentence segmentation and scoring, named entity recognition, text part-of-speech (POS) analysis, anaphora detection, diversity content measure as well as sentence reordering. This paper provides a full summary report on the four-year long task. While yearly overviews focused on system results, in this paper we provide a detailed report on the approaches proposed by the participants and which can be considered as the state of the art for this task. As an important result from the 4 years competition, we also describe the open access resources that have been built and collected. The evaluation measures for automatic summarization designed in DUC or MUC were not appropriate to evaluate tweet contextualization, we explain why and depict in detailed the LogSim measure used to evaluate informativeness of produced contexts or summaries. Finally, we also mention the lessons we learned and that it is worth considering when designing a task

Suggestion contextuelle composite

International audienceLa suggestion contextuelle consiste à recommander à un utilisateur un ensemble de lieux d'activités adaptés à ses préférences et à son contexte. La plupart des approches existantes considèrent uniquement ces deux caractéristiques pour constituer leur liste de suggestions. Cependant, les recherches en systèmes de recommandation ont récemment souligné l'importance de la diversité des suggestions. Cet article présente un modèle novateur de suggestion contextuelle inspiré de la recherche composite qui consiste à regrouper les suggestions en différentes grappes thématiquement cohésives. L'évaluation réalisée dans le cadre de la piste Contextual Suggestion de TREC 2013 et 2014 montre que notre approche est compétitive et permet d'améliorer la diversité des suggestions sans dégrader leur pertinence

Using Semantic-Based User Profile Modeling for Context-Aware Personalised Place Recommendations

Place Recommendation Systems (PRS's) are used to recommend places to visit to World Wide Web users. Existing PRS's are still limited by several problems, some of which are the problem of recommending similar set of places to different users (Lack of Personalization) and no diversity in the set of recommended items (Content Overspecialization). One of the main objectives in the PRS's or Contextual suggestion systems is to fill the semantic gap among the queries and suggestions and going beyond keywords matching. To address these issues, in this study we attempt to build a personalized context-aware place recommender system using semantic-based user profile modeling to address the limitations of current user profile building techniques and to improve the retrieval performance of personalized place recommender system. This approach consists of building a place ontology based on the Open Directory Project (ODP), a hierarchical ontology scheme for organizing websites. We model a semantic user profile from the place concepts extracted from place ontology and weighted according to their semantic relatedness to user interests. The semantic user profile is then exploited to devise a personalized recommendation by re-ranking process of initial search results for improving retrieval performance. We evaluate this approach on dataset obtained using Google Paces API. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based place recommendation model

CWI and TU Delft at TREC 2013: Contextual Suggestion, Federated Web Search, KBA, and Web Tracks

This paper provides an overview of the work done at the Centrum Wiskunde & Informatica (CWI) and Delft University of Technology (TU Delft) for different tracks of TREC 2013. We participated in the Contextual Suggestion Track, the Federated Web Search Track, the Knowledge Base Acceleration (KBA) Track, and the Web Ad-hoc Track. In the Contextual Suggestion track, we focused on filtering the entire ClueWeb12 collection to generate recommendations according to the provided user profiles and contexts. For the Federated Web Search track, we exploited both categories from ODP and document relevance to merge result lists. In the KBA track, we focused on the Cumulative Citation Recommendation task where we exploited different features to two classification algorithms. For the Web track, we extended an ad-hoc baseline with a proximity model that promotes documents in which the query terms are positioned closer together

An Evaluation of Contextual Suggestion

This thesis examines techniques that can be used to evaluate systems that solve the complex task of suggesting points of interest to users. A traveller visiting an unfamiliar, foreign city might be looking for a place to have fun in the last few hours before returning home. Our traveller might browse various search engines and travel websites to find something that he is interested in doing, however this process is time consuming and the visitor may want to find some suggestion quickly. We will consider the type of system that is able to handle this complex request in such a way that the user is satisfied. Because the type of suggestion one person wants will differ from the type of suggestion another person wants we will consider systems that incorporate some level of personalization. In this work we will develop user profiles that are based on real users and set up experiments that many research groups can participate in, competing to develop the best techniques for implementing this kind of system. These systems will make suggestion of attractions to visit in various different US cities to many users. This thesis is divided into two stages. During the first stage we will look at what information will go into our user profiles and what information we need to know about the users in order to decide whether they would visit an attraction. The second stage will be deciding how to evaluate the suggestions that various systems make in order to determine which system is able to make the best suggestions

Aggregated search: a new information retrieval paradigm

International audienceTraditional search engines return ranked lists of search results. It is up to the user to scroll this list, scan within different documents and assemble information that fulfill his/her information need. Aggregated search represents a new class of approaches where the information is not only retrieved but also assembled. This is the current evolution in Web search, where diverse content (images, videos, ...) and relational content (similar entities, features) are included in search results. In this survey, we propose a simple analysis framework for aggregated search and an overview of existing work. We start with related work in related domains such as federated search, natural language generation and question answering. Then we focus on more recent trends namely cross vertical aggregated search and relational aggregated search which are already present in current Web search