Towards Improving Recommender System: A Social Trust-Aware Approach

Abstract

Abstract-Recommender systems have shown great potential to help users find interesting and relevant Web service (WS) from within large registers. However, with the proliferation of WSs, recommendation becomes a very difficult task. Social computing seems offering innovative solutions to overcome those shortcomings. Social computing is at the crossroad of computer sciences and social sciences disciplines by looking into ways of improving application design and development using elements that people encounter daily such as social networks, trust, reputation, and recommendation. In this paper, we propose a social trust-aware system for recommending Web services (WSs) based on social qualities of WSs that they exhibit towards peers at runtime, and trustworthiness of the users who provide feedback on their overall experience using WSs. A set of experiments to assess the fairness and accuracy of the proposed system are reported in the paper, showing promising results and demonstrating that our service recommendation method significantly outperforms conventional similarity-based and trust-based service recommendation methods.

Survey on Personalized Web Recommender System

“…Demographic recommender assumes that customers with same demographic data will rate the products similarly and recommender obtains cluster of similar demographic data. For example if two users user1 and user2 share most probably the same demographic data and give rating to same product then demographic recommender system will obtain cluster of similar demographic data [30]. Here demographic data is needed to forecast best rating for recommendation of items to the customers [28].…”

Towards Semantics-Aware Recommender System: A LOD-Based Approach

“…DBpedia is one of the main projects of the Linked Open Data community, which "focuses on the task of converting Wikipedia content into structured knowledge, such that Semantic Web techniques can be employed against it" [16]. Our system maintains one or more SPARQL Endpoints to various LOD datasets, and then it harvest the description of each item of the system from these endpoints.…”

“…C1 (5,1,3,3,2) Considering the threshold= 2.5, we will retain respectively the paths C 4 , C 6 , C 7 , and C 8 .…”

“…The quality of recommendations is dependent on the nature of details (in terms of quality and quantity) available to users [1]. The data available on the web are in large quantities that needs a filtering to make the best data.…”

Recommendation Techniques in Mobile Learning Context: A Review

“…For example, we can cite "FilmTrust" or "TrustedOpion" that recommends restaurants, cafes, bars and movies through a website. These social recommendation forms are organized into two main steps: (1) the construction of a model of confidence and (2) use of a computational model to estimate the level of interest of an object to an individual [32] [33].…”