Recommender systems help customers to choose right product or service from large number of alternatives available on Internet. In recent time, trust becomes an important issue in designing effective recommender systems. In this paper we have studied the role of trust and distrust in designing recommender systems. General Terms E-Commerce, Information Retrieval, Web Mining.

This study investigates how consumers assess the quality o f two types o f recommender systems , co llaborative filtering and content -based, in the content of e-commerce by using a modified Unified Theory o f Acceptance and Use o f Techno logy (UTAUT) model. Specifically, the under-investigated concept o f trust in techno log ical artifacts is adap ted to a modified UTAUT model. Additionally, ...

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification...

Recommender systems assist and augment this natural social process. In a typical recommender system people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients. In some cases the primary transformation is in the aggregation; in others the system’s value lies in its ability to make good matches between the recommenders and those seeking recomm...

Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vector. Nowadays e-commercial platforms provide various kinds attribute information for items (e.g., category, price, and style clothing). Utilizing this better is popular recent years. Some studies use given side informa...

Modern recommender systems (RS) work by processing a number of signals that can be inferred from large sets user-item interaction data. The main signal to analyze stems the raw matrix represents interactions. However, we increase performance RS considering other kinds like context interactions, which could be, for example, time or date interaction, user location, sequential data corresponding h...

Reproducibility is a challenging aspect that considerably affects the quality of most scientific papers. To deal with this, many open frameworks allow to build, test, and benchmark recommender systems for single users. Group involve additional tasks w.r.t. those users, such as identification groups, or their modeling. While this clearly amplifies possible reproducibility issues, date, no framew...