Collaborative recommender system has been an important and popular approach in making recommendations. However, it suffers with the cold start and sparsity problems. Therefore, to alleviate the problems, we have combined a set similarity and user evaluation method in collaborative filtering by introducing some additional weight parameters. Further, we optimize these parameters by Particle Swarm...

Collaborative filtering, a widely-used user-centric recommendation technique, predicts an item’s rating by aggregating its ratings from similar users. User similarity is usually calculated by cosine similarity or Pearson correlation coefficient. However, both of them consider only the direction of rating vectors, and suffer from a range of drawbacks. To solve these issues, we propose a novel Ba...

Similarity measurement between web services is a key solution to benefit from the reuse of the large number of web services freely available in the internet. This paper presents a practical approach that enables an effective measurement of web service similarity based on their interfaces descibed with WSDL. The approach relies on the use of multiple matching techniques and different semantic an...

The Mental Lexicon (ML) refers to the organization of lexical entries of a language in the human mind.A clear knowledge of the structure of ML will help us to understand how the human brain processes language. The knowledge of semantic association among the words in ML is essential to many applications. Although, there are works on the representation of lexical entries based on their semantic a...

We describe our efforts in TREC 2012 Contextual Suggestion Track. The task is to provide suggestions to users based on their personal interests as well as their contexts. To tackle the problem, we propose to rank candidate suggestions based on their similarity to the personal profile and that to the contexts (i.e., geographic and temporal information). The ranking function is computed based on ...

We present an interactive system that allows a user to locate regions of video that are similar to a video query. Thus segments of video can be found by simply providing an example of the video of interest. The user selects a video segment for the query from either a static frame-based interface or a video player. A statistical model of the query is calculated on-the-fly, and is used to find si...

Nowadays, the management of sequential and temporal data is an increasing need in many data mining processes. Therefore, the development of new privacy preserving data mining techniques for sequential data is a crucial need to ensure that sequence data analysis is performed without disclosure sensitive information. Although data analysis and protection are very different processes, they share a...

Mining user patterns of log files can provide significant and useful informative knowledge. This paper present an approach for mining similarity of interest among web users from their past access behaviors. Unlike traditional clustering methods that focus on grouping objects with similar values on a set of dimensions, clustering by pattern similarity finds objects that exhibit a coherent patter...

We present a new similarity measure tailored to posts in an online forum. Our measure takes into account all the available information about user interest and interaction — the content of posts, the threads in the forum, and the author of the posts. We use this post similarity to build a similarity between users, based on principal coordinate analysis. This allows easy visualization of the user...

Extracting the relevant information by exploiting the spatial data warehouse becomes increasingly hard. In fact, because of the enormous amount of data stored in the spatial data warehouse, the user, usually, don't know what part of the cube contain the relevant information and what the forthcoming query should be. As a solution, we propose to study the similarity between the behaviors of the u...