A social recommender mechanism for location-based group commerce

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

“…Instead of the classical individual transactions such as C2C and B2C, users now utilize the advantages of social influence and price discount of group commerce to get the lowest price possible as the larger the volume of product bought the lower the unit price. The authors of [60] proposed a group-coupon recommendation mechanism that identifies and analyses three factors to target customers who are interested in a similar product and are geographically close to the retailer to assure successful group transactions. These factors are the individual preferences with respect to the product characteristics, the geographical convenience of the store and the social influence of the product.…”

An ordinal ranking criterion for the subjective evaluation of alternatives and exchange reliability

“…This type of information structure applies also to online search environments, where consumers are presented with a set of product characteristics displayed within several links when browsing through different websites [10]. (b) A second area of application follows from online search environments and concentrates on the agent recommendations provided to the DMs based on their purchasing history [35,40,43]. These recommendations involve the description of a series of product characteristics by unknown third parties to the DM.…”

Simultaneous co-clustering and learning to address the cold start problem in recommender systems

“…More recently, RSs have also been used to recommend movies [6], songs [17], videos [12], research resources in digital libraries [40,41], and people one may know from social networks [10,32]. To build their recommendations, RSs use varied data sources, which define the characteristics of items, users, and their transactions, and are categorized by the data sources and techniques used, such as, Content Based Filtering (CBF), Demographic Filtering (DF), and Collaborative Filtering (CF).…”