Xuan Hau Pham

-Spear: A New Method for Expert Based Recommendation Systems

Recommendation systems are based on a fast and effective personalized mechanism to provide items relevant to users. In this article, an expert-based approach for recommendation is proposed. We extend the spamming-resistant expertise analysis and ranking (SPEAR) algorithm to determine a set of experts from a set of attributes and values, calling the modification the -SPEAR algorithm. This system can recommend a set of items to users using expert opinions. In this approach, we use ontology to build profiles of users. The experimental results are implemented in the movie domain as a case study. Our data set was collected from IMDB and MovieLens data sets.

Integrating multiple experts for correction process in interactive recommendation systems

To improve the performance of the recommendation process, most of recommendation systems (RecSys) should collect better ratings from users. Particularly, rating process is an important task in interactive RecSys which can ask users to correct their own ratings. However, in real world, there are many inconsistencies (e.g., mistakes and missing values) or incorrect in the user ratings. Thereby, expert-based recommendation framework has been studied to select the most relevant experts in a certain item attribute (or value). This kind of RecSys can i) discover user preference and ii) determine a set of experts based on attribute and value of items. In this paper, we propose a consensual recommendation framework integrating multiple experts to conduct correction process. Since the ratings from experts are assumed to be reliable and correct, we first analyze user profile to determine the preference and find out a set of experts. Next, we measure a minimal inconsistency interval (MinIncInt) that might contain incorrect ratings. Finally, we propose solutions to correct the incorrect rating based on ratings from multiple experts.

Extending HITS Algorithm for Ranking Locations by Using Geotagged Resources

The paper focuses on using geotagged resources from the social network service (SNS) for searching the famous places from keyword. We extend the HITS[9] algorithm in order to rank locations which are collected from geotagged resources on SNS. Our approach not only uses the similarity measurement between locations’tags for computing the value of locations but also calculate the term frequency of tags which occur in each location to modify the value of tags for ranking. We implement and show the experimental results with the set of locations from the geotagged resources.

Social Context-Based Movie Recommendation: A Case Study on MyMovieHistory

Social networking services (in short, SNS) allow users to share their own data with family, friends, and communities. Since there are many kinds of information that has been uploaded and shared through the SNS, the amount of information on the SNS keeps increasing exponentially. Particularly, Facebook has adopted some interesting features related to entertainment (e.g., movie, music and TV show). However, they do not consider contextual information of users for recommendation (e.g., time, location, and social contexts). Therefore, in this paper, we propose a novel approach for movie recommendation based on the integration of a variety contextual information (i.e., when the users watched the movies, where the users watched the movies, and who watched the movie with them). Thus, we developed a Facebook application (called MyMovieHistory) for recording the movie history of users and recommending relevant movies.

Preference-based user rate correction process for interactive recommendation systems

In recommendation systems, rating is an important user activity reflecting their opinions. Once the users return their rates about the items suggested from the systems, the user rates can be used to adjust recommendation process. However, users can make some mistakes (e.g., nature noises) during rating the items. As the recommendation systems receive more incorrect rates, the performance of such systems might be decreased. To solve the problem, in this paper, we focus on an interactive recommendation system which can help users to correct their own rates.

User Timeline and Interest-Based Collaborative Filtering on Social Network

A lot of users and large amount of information have been posted and shared through on-line systems. User timeline and interest are important features on recommendation systems (e.g., user likes watching action movies in the morning, and likes watching drama movies in the afternoon however he/she likes watching thriller movies in the evening) and also on social network. There are some recommendation applications have been developed on social network to support users selecting what kind of wanted items based on user timeline and interest. However, there is not any approaches based on user timeline and interest have been proposed that user interest have been separated into partitions of user interest. Thus, a recommendation mechanism will be applied on social networks based on extracting user timeline and user interest that is necessary. In this paper, we propose a new approach that user interest will be determined on a set of time partitions.

Lexical Matching-Based Approach for Multilingual Movie Recommendation Systems

Recommendation systems (RecSys) have been developed for personalized users interaction process to deal with overload information. Movie Content-based recommendation approaches try to measure similarity between movie or users based on relevant information. Nowadays the amount of information on the web exists in several languages. The items description on the RecSys may be not only native languages but also multilingualism. Besides, users interact to the system come from many countries in different languages. However, most of these recommendation systems lack mechanisms to support users overcoming the language problem. Thus, in this paper, we propose a lexical matching-based approach to deal with multilingualism in our process and show efficient experiment for multilingual recommendation system in movie domain.

Context-Based Traffic Recommendation System

In this paper, we propose a new traffic system recommendation based on support real-time flows in highly unpredictable sensor network environments. The approach system is real-time recommendation system which meet various demands of users. The proposed algorithm include two phases. First phase is proposed to deal with the real-time problem. By this way, the drivers are able to transfer on the way with the shortest-time. For second phase, a research algorithm based on Depth First Search (DFS) algorithm will recommend the paths which meet demands of drivers based their context such as the paths with include the famous landscapes or the paths where they can find out good restaurants for their break while driving.

An Black-Box Testing Approach on User Modeling in Practical Movie Recommendation Systems

Since there have been many practical recommendation services in real world, main research questions are i) how such services provide users with recommendations, and ii) how they are different from each other. The aim of this paper is to evaluate user modeling process in several practical recommendation systems. Black-box testing scheme has been applied by comparing recommendation results. User models (i.e., a set of user ratings) have been synthesized to discriminate the recommendation results. Particularly, we focus on investigating whether the services consider attribute selection.

Understanding information propagation on online social tagging systems: a case study on flickr

Social media have been one of the most popular online communication channels to share information among users. It means the users can give (and take) cognitive influence to (and from) the others. Thus, it is important for many applications to understand how the information can be propagated. In this paper, we focus on social tagging systems where users can easily exchange tags with each other. To conduct experimentation, a tag search system has been implemented to collect a dataset from Flickr.