In this paper we report on a study of implicit feedback models for unobtrusively tracking the information needs of searchers. Such models use relevance information gathered from searcher interaction and can be a potential substitute for explicit relevance feedback. We introduce a variety of implicit feedback models designed to enhance an Information Retrieval (IR) system’s representation of sea...

Collaborative €ltering (CF) is one of themost ecient ways for recommender systems. Typically, CF-based algorithms analyze users’ preferences and items’ aŠributes using one of two types of feedback: explicit feedback (e.g., ratings given to item by users, like/dislike) or implicit feedback (e.g., clicks, views, purchases). Explicit feedback is reliable but is extremely sparse; whereas implicit ...

Measuring the quality of recommendations produced by a recommender system (RS) is challenging. Labels used for the evaluation are typically obtained from users of a RS; such explicit labels reflect true user preferences but may introduce significant biases in the evaluation process. In this paper, we investigate biases that may affect labels inferred from implicit feedback, such as clicks or ot...

This study used archival data to examine how White, Black, and biracial Black/White people respond to implicit attitude feedback suggesting that they harbor racial bias that does not align with their self-reported attitudes. The results suggested that people are generally defensive in response to feedback indicating that their implicit attitudes differ from their explicit attitudes. Among monor...

Our goal in this study was to explore the potentials of extracting features from eye-tracking data that have the potential to improve performance in implicit relevance feedback. We view this type of data as an example of the searcher’ immediate context and as containing useful clues of the indications of the interaction between the searcher and the IR system. In particular, we explored if we co...

Abstract The recommender system (RS) has played an increasingly important role in Internet applications. Recent literature on RS mainly focused better fitting the user behavior data. However, data is observational, not experimental. This makes for a wide range of biases In this paper, we introduce novel framework to combine advantages both multi-task and curriculum learning debiased recommendat...

In the area of information retrieval the concept of relevance feedback is used to provide high relevant documents to the user. The process of gaining relevance data is usually based on explicit Relevance Feedback. But it turned out, that users are usually not willing to provide such data. This paper describes a Relevance Feedback approach that supports the users with query expansion terms by us...