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...

Recommender systems are software applications that help users to find items of interest in situations information overload. Current research often assumes a one-shot interaction paradigm, where the users’ preferences estimated based on past observed behavior and presentation ranked list suggestions is main, one-directional form user interaction. Conversational recommender (CRS) take different a...

Recommender systems are a subclass of information filtering systems that predict the 'rating' or 'preference' that a user would give to an item. Most traditional Recommender Systems (RSs) focus on recommending the most relevant items to individual users and do not take into consideration the circumstances and other contextual information such as time, place and company of other people when reco...