The present study aimed at investigating the effects of explicit and implicit recasts on Iranian EFL learners' acquisition of English relative clauses. For this purpose, 64 participants were selected out of 94 intermediate level EFL learners at Falagh language Institute, Rasht, Iran. To have homogenized groups, the researcher administered a language proficiency test (TOEFL). Then, the researche...

User preferences for items can be inferred from either explicit feedback, such as item ratings, or implicit feedback, such as rental histories. Research in collaborative filtering has concentrated on explicit feedback, resulting in the development of accurate and scalable models. However, since explicit feedback is often difficult to collect it is important to develop effective models that take...

One common dichotomy faced in recommender systems is that explicit user feedback -in the form of ratings, tags, or user-provided personal informationis scarce, yet the most popular source of information in most state-of-the-art recommendation algorithms, and on the other side, implicit user feedback such as numbers of clicks, playcounts, or web pages visited in a sessionis more frequently avail...

Recommenders personalize the web content by typically using collaborative filtering to relate users (or items) based on explicit feedback, e.g., ratings. The difficulty of collecting this feedback has recently motivated to consider implicit feedback (e.g., item consumption along with the corresponding time). In this paper, we introduce the notion of consumed item pack (CIP) which enables to lin...

This literature review provides an overview of the various topics related to using implicit feedback for information retrieval. Specific topics which are addressed are explicit relevance feedback for query expansion and its advantages and disadvantages, user behaviors that provide implicit information to a system, techniques to implement implicit feedback in systems and the advantages and disad...

Measuring the quality of recommendations produced by a recommender system (RS) is challenging. Labels used for evaluation are typically obtained from users of a RS, by asking for explicit feedback, or inferring labels from implicit feedback. Both approaches can introduce significant biases in the evaluation process. We investigate biases that may affect labels inferred from implicit feedback. I...