Personalization of web-search using short-term browsing context

Abstract

Search and browsing activity is known to be a valuable source of information about user's search intent. It is extensively utilized by most of modern search engines to improve ranking by constructing certain ranking features as well as by personalizing search. Personalization aims at two major goals: extraction of stable preferences of a user and specification and disambiguation of the current query. The common way to approach these problems is to extract information from user's search and browsing long-term history and to utilize short-term history to determine the context of a given query. Personalization of the web search for the first queries in new search sessions of new users is more difficult due to the lack of both long-and short-term data.In this paper we study the problem of short-term personalization. To be more precise, we restrict our attention to the set of initial queries of search sessions. These, with the lack of contextual information, are known to be the most challenging for short-term personalization and are not covered by previous studies on the subject. To approach this problem in the absence of the search context, we employ short-term browsing context. We apply a widespread framework for personalization of search results based on the reranking approach and evaluate our methods on the large scale data. The proposed methods are shown to significantly improve non-personalized ranking of one of the major commercial search engines. To the best of our knowledge this is the first study addressing the problem of short-term personalization based on recent browsing history. We find that performance of this re-ranking approach can be reasonably predicted given a query. When we restrict the use of our method to the queries with largest expected gain, the resulting benefit of personalization increases significantly.

A Deep Look into neural ranking models for information retrieval

“…• Short-term history: the user past interactions with the system in the current search session [142,143,144].…”

Attentive Long Short-Term Preference Modeling for Personalized Product Search

“…It involves two important problems, namely, how to determine the duration of a session and how to learn the user preference in a session. To solve these problems, Sriram et al [55] used temporal closeness and probabilistic similarities between queries to determine the duration of a session; Daoud et al [14,15] leveraged a predefined ontology of semantic concepts to construct the short-term user profile; methods in [58,61] determine the extent of using context (e.g., prior queries and clicks) and investigate the combination of the query and the context to model the user intention; and Shen et al [52] captured users' interests by analyzing the immediate contexts and implicit feedback.…”

Personalization in text information retrieval: A survey

“…Besides search behaviors in one search session (instant and short term), research has also considered search history in previous sessions (long term) (e.g., Albanese, Picariello, Sansone, & Sansone, 2004;Sontag et al, 2012;Ustinovskiy & Serdyukov, 2013;Wedig & Madani, 2006). Regarding the usefulness of short-and long-term behaviors for personalization, Zhu, Callan, and Carbonell (2008) found that incorporating short-term contexts works well for personalized search, and longer history does not provide further improvement.…”