Two key, but usually ignored, issues for the evaluation of methods of personalization for information retrieval are: that such evaluation must be of a search session as a whole; and, that people, during the course of an information search session, engage in a variety of activities, intended to accomplish differ- ent goals or intentions. Taking serious account of these factors has major impli- cations for not only evaluation methods and metrics, but also for the nature of the data that is necessary both for understanding and modeling information search, and for evaluation of personalized support for information retrieval (IR). In this position paper, we: present a model of IR demonstrating why these fac- tors are important; identify some implications of accepting their validity; and, on the basis of a series of studies in interactive IR, identify some types of data concerning searcher and system behavior that we claim are, at least, necessary, if not necessarily sufficient, for meaningful evaluation of personalization of IR.
Personalization despite being an effective solution to the problem information overload remains tricky on account of multiple dimensions to consider. Furthermore, the challenge of avoiding overdoing personalization involves estimation of a user's preferences in relation to different queries. This work is an attempt to make inferences about when personalization would be beneficial by relating observable user behavior to his/her social network usage patterns and user-generated content. User behavior on a search system is observed by means of team-draft interleaving whereby results from two retri…
Document ranking based on probabilistic evaluations of relevance is known to exhibit non-classical correlations, which may be explained by admitting a complex structure of the event space, namely, by assuming the events to emerge from multiple sample spaces. The structure of event space formed by overlapping sample spaces is known in quantum mechanics, they may exhibit some counter-intuitive features, called quantum contextuality. In this Note I observe that from the structural point of view quantum contextuality looks similar to personalization of information retrieval scenarios. Along these …
Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization
McGill University（麦吉尔大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Université de Montréal（蒙特利尔大学）；Salesforce（Salesforce公司）；HEC Montréal（蒙特利尔HEC商学院）；Mila - Quebec AI Institute（魁北克人工智能研究所）
Beyond Retrieval: Learning Compact User Representations for Scalable LLM Personalization
Microsoft（微软公司）；Shanghai International Studies University（上海国际问题研究大学）；Zhejiang University（浙江大学）；Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（数据科学与人工智能系，香港理工大学）
The Personalization of information has taken recommender systems at a very high level. With personalization these systems can generate user specific recommendations accurately and efficiently. User profiling helps personalization, where information retrieval is done to personalize a scenario which maintains a separate user profile for individual user. The main objective of this paper is to explore this field of personalization in context of user profiling, to help researchers make aware of the user profiling. Various trends, techniques and Applications have been discussed in paper which will f…
Personalization customizes information access. The PIPE ("Personalization is Partial Evaluation") modeling methodology represents interaction with an information space as a program. The program is then specialized to a user's known interests or information seeking activity by the technique of partial evaluation. In this paper, we elaborate PIPE by considering requirements analysis in the personalization lifecycle. We investigate the use of scenarios as a means of identifying and analyzing personalization requirements. As our first result, we show how designing a PIPE representation can be cast…
Product search is an important way for people to browse and purchase items on E-commerce platforms. While customers tend to make choices based on their personal tastes and preferences, analysis of commercial product search logs has shown that personalization does not always improve product search quality. Most existing product search techniques, however, conduct undifferentiated personalization across search sessions. They either use a fixed coefficient to control the influence of personalization or let personalization take effect all the time with an attention mechanism. The only notable exce…
Product search is one of the most popular methods for people to discover and purchase products on e-commerce websites. Because personal preferences often have an important influence on the purchase decision of each customer, it is intuitive that personalization should be beneficial for product search engines. While synthetic experiments from previous studies show that purchase histories are useful for identifying the individual intent of each product search session, the effect of personalization on product search in practice, however, remains mostly unknown. In this paper, we formulate the pro…
Typical retrieval systems have three requirements: a) Accurate retrieval i.e., the method should have high precision, b) Diverse retrieval, i.e., the obtained set of points should be diverse, c) Retrieval time should be small. However, most of the existing methods address only one or two of the above mentioned requirements. In this work, we present a method based on randomized locality sensitive hashing which tries to address all of the above requirements simultaneously. While earlier hashing approaches considered approximate retrieval to be acceptable only for the sake of efficiency, we argue…
Facebook（脸书）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
With the rise of social networks, information on the internet is no longer solely organized by web pages. Rather, content is generated and shared among users and organized around their social relations on social networks. This presents new challenges to information retrieval systems. On a social network search system, the generation of result sets not only needs to consider keyword matches, like a traditional web search engine does, but it also needs to take into account the searcher's social connections and the content's visibility settings. Besides, search ranking should be able to handle bo…
In large-scale e-commerce platforms like Taobao, it is a big challenge to retrieve products that satisfy users from billions of candidates. This has been a common concern of academia and industry. Recently, plenty of works in this domain have achieved significant improvements by enhancing embedding-based retrieval (EBR) methods, including the Multi-Grained Deep Semantic Product Retrieval (MGDSPR) model [16] in Taobao search engine. However, we find that MGDSPR still has problems of poor relevance and weak personalization compared to other retrieval methods in our online system, such as lexical…
