User-to-user similarity is a fundamental component of Collaborative Filtering (CF) recommender systems. In user-to-user similarity the ratings assigned by two users to a set of items are pairwise compared and averaged (correlation). In this paper we make user-touser similarity adaptive, i.e., we dynamically change the computation depending on the profiles of the compared users and the item whos...

Recommender systems could be seen as an application of a data mining process in which data collection, pre-processing, building user profiles and evaluation phases are performed in order to deliver personalised recommendations. Collaborative filtering systems rely on user-to-user similarities using standard similarity measures. The symmetry of most standard similarity measures makes it difficul...

User similarity measurement plays a key role in collaborative filtering recommendation which is the most widely applied technique in recommender systems. Traditional user-based collaborative filtering recommendation methods focus on absolute rating difference of common rated items while neglecting the relative rating level difference to the same items. In order to overcome this drawback, we pro...

  One of the most famous methods for recommendation is user-based Collaborative Filtering (CF). This system compares active user’s items rating with historical rating records of other users to find similar users and recommending items which seems interesting to these similar users and have not been rated by the active user. As a way of computing recommendations, the ultimate goal of the user-ba...

This article reviews the problem of degree of closeness and interaction level in a social network by ranking users based on similarity score. This similarity is measured on the basis of social, geographic, educational, professional, shared interests, pages liked, mutual interested groups or communities and mutual friends. The technique addresses the problem of matching user profiles in its glob...

Lightweight Mashup service become very prevalent now since there are lots of advantages for them, such as easy use, short development time, and strong scalability. It is a challenge problem how to recommend user-interested, high-quality Mashup services to user with the rapid development of more and more Mashup service. In this paper, we propose CSCF (a Mashup service recommendation approach bas...

Abstract: In this paper, we discuss the similarity between two trajectories using Needleman Wunsch algorithm. The calculation steps are interpolating trajectory, calculating distance trajectory coordinates, identifying equivalent length, transforming into a sequence of alphabetic letters, aligning sequences, and measuring magnitude based on alignment results. obtained is compared directly to le...

Most existing reciprocal recommender systems use either profile similarity or interaction similarity to recommend new matches, assuming that user preferences are static and ignoring temporal aspects of user behaviour. This paper takes a different approach, and addresses the issue of representing user preferences as dynamic. We introduce a new representation for changes in user preferences and u...

When facing great volume of query results while users are searching literatures on the Web, we propose to refine the search process by using user interests. We analyze user interests and calculate semantic similarity among those interest terms to fulfill query refinement. Traditional general purpose similarity measures may not always fit a domain specific context. In this paper, under the conte...