Relevance Feedback and Personalization: A Language Modeling Perspective
نویسندگان
چکیده
Many approaches to personalization involve learning short-term and long-term user models. The user models provide context for queries and other interactions with the information system. In this paper, we discuss how language models can be used to represent context and support context-based techniques such as relevance feedback and query disambiguation.
منابع مشابه
Nonintrusive Personalization in Interactive Information Retrieval
Information retrieval systems are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users; information about the actual user and search context is largely ignored. Personalization is expected to break this deficiency and significantly improve retrieval accuracy. However, the...
متن کاملLanguage Modeling Based Local Set Reranking using Manual Relevance Feedback
We present a novel approach to re-ranking documents using language modeling (LM) and manual relevance feedback (RF). The documents returned by an initial search algorithm, called the Local Set, is reranked based on manual relevance feedback using a ranking function modified to perform at the local set level. Instead of using the query independent collection model, which is too general, we use t...
متن کاملA Study of Language Models for Exploiting User Feedback in Information Retrieval By
Feedback is an important technique in Information Retrieval to have users provide contextual information about their search needs, with the goal of improving retrieval accuracy and achieving personalization. Relevance feedback has been studied extensively, and in recent years new types of feedback such as implicit feedback and collective feedback have attracted much research interest. However, ...
متن کاملUser-Centered Adaptive Information Retrieval
Information retrieval systems are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users; information about the actual user and search context is largely ignored. Personalization is expected to break this deficiency and significantly improve retrieval accuracy. In this thes...
متن کاملModeling the Evolution of Context in Information Retrieval
An Information Retrieval (IR) system ranks documents according to their predicted relevance to a formulated query. The prediction depends on the ranking algorithm adopted and on the assumptions about relevance underlying the algorithm. The main assumption is that there is one user, one information need for each query, one location where the user is, and no temporal dimension. But this assumptio...
متن کامل