Mobile Multimedia Recommendation in Smart Communities: A Survey <s> Model Based <s> This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations. <s> BIB001 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> Model Based <s> Recommender technologies are crucial for the effective support of customers in online sales situations. The state-of-the-art research in recommender systems is not aware of existing theories in the areas of cognitive and decision psychology and thus lacks of deeper understanding of online buying situations. In this paper we present results from user studies related to serial position effects in human memory in the context of knowledge-based recommender applications. We discuss serial position effects on the recall of product descriptions as well as on the probability of product selection. Serial position effects such as primacy and recency are major building blocks of persuasive, next generation knowledge-based recommender systems. <s> BIB002
Recommended Multimedia Resources Figure 4 . Mobile multimedia recommendation system framework they prefer to generate a recommendation based on matching between user's need, preferences and set of items available. Knowledge-based recommender systems can answer to the question of how special items can meet the special user's need by applying knowledge BIB001 - BIB002 .