Inay Ha

Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation

Abstract We propose a collaborative filtering method to provide an enhanced recommendation quality derived from user-created tags. Collaborative tagging is employed as an approach in order to grasp and filter users’ preferences for items. In addition, we explore several advantages of collaborative tagging for data sparseness and a cold-start user. These applications are notable challenges in collaborative filtering. We present empirical experiments using a real dataset from del . icio . us . Experimental results show that the proposed algorithm offers significant advantages both in terms of improving the recommendation quality for sparse data and in dealing with cold-start users as compared to existing work.

Collaborative user modeling for enhanced content filtering in recommender systems

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of content suited to their needs. To provide proper recommendations to users, personalized recommender systems require accurate user models of characteristics, preferences and needs. In this study, we propose a collaborative approach to user modeling for enhancing personalized recommendations to users. Our approach first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user model based on collaborative filtering approaches, and a vector space model. We present experimental results that show how our model performs better than existing alternatives.

Alleviating the cold-start problem by incorporating movies facebook pages

Recommender systems are generally known as predictive ecosystem which recommends an appropriate list of items that may imply their similar preference or interest. Nevertheless, most discussed issues in recommendation system research domain are the cold-start problem. In this paper we proposed a novel approach to address this problem by combining similarity values obtain from a movie “Facebook Pages”. To achieve this, we first compute users’ similarity according to the rating cast on our Movie Rating System. Then, we combined similarity value obtain from user’s genre interest in “Like” information extracted from “Facebook Pages”. Finally, all the similarity values are combined to produce a new user’s similarity value. Our experiment results show that our approach is outperformed in cold-start problem compared to the benchmark algorithms. To evaluate whether our system is strong enough to recommend higher accuracy recommendation to users, we also conducted prediction coverage in this research work.

User preference modeling from positive contents for personalized recommendation

With the spread of the Web, users can obtain a wide variety of information, and also can access novel content in real time. In this environment, finding useful information from a huge amount of available content becomes a time consuming process. In this paper, we focus on user modeling for personalization to recommend content relevant to user interests. Techniques used for association rules in deriving user profiles are exploited for discovering useful and meaningful patterns of users. Each user preference is presented the frequent term patterns, collectively called PTP (Personalized Term Pattern) and the preference terms, called PT (Personalized Term). In addition, a content-based filtering approach is employed to recommend content corresponding with user preferences. In order to evaluate the performance of the proposed method, we compare experimental results with those of a probabilistic learning model and vector space model. The experimental evaluation on NSF research award datasets demonstrates that the proposed method brings significant advantages in terms of improving the recommendation quality in comparison with the other methods.

Personalized advertisement system using social relationship based user modeling

The influence of social relationships has received considerable attention in recommendation systems. In this paper, we propose a personalized advertisement recommendation system based on user preference and social network information. The proposed system uses collaborative filtering and frequent pattern network techniques using social network information to recommend personalized advertisements. Frequent pattern network is employed to alleviate cold-start and sparsity problems of collaborative filtering. For the social relationship modeling, direct and indirect relations are considered and relation weight between users is calculated by using six degrees of Kevin Bacon. Weight ‘1’ is given to those who have connections directly, and weight ‘0’ is given to those who are over six steps away and hove no relation to each other. According to a research of Kevin Bacon, everybody can know certain people through six depths of people. In order to improve prediction accuracy, we apply social relationship to user modeling. In our experiments, advertisement information is collected and item rating and user information including social relations are extracted from a social network service. The proposed system applies user modeling between collaborative filtering and frequent pattern network model to recommend advertisements according to user condition. User’s types are composed with combinations of both techniques. We compare the performance of the proposed method with that of other methods. From the experimental results, a proposed system applying user modeling using social relationships can achieve better performance and recommendation quality than other recommendation systems.

Social filtering using social relationship for movie recommendation

Traditional recommendation systems provide appropriate information to a target user after analyzing user preferences based on user profiles and rating histories. However, most of people also consider the friends opinions when they purchase some products or watch the movies. As social network services have been recently popularized, many users obtain and exchange their opinions on social networks. This information is reliable because they have close relationships and trust each other. Most of the users are satisfied with the information. In this paper, we propose a recommendation system based on advanced user modeling using social relationship of users. For the user modeling, both direct and indirect relations are considered and the relation weight between users is calculated by using six degrees of Kevin Bacon. From the experimental results, our proposed social filtering method can achieve better performance than a traditional user-based collaborative filtering method.

Semantic Analysis of User Behaviors for Detecting Spam Mail

According to continuous increasing of spam email, 92.6% of recent total email is known spam email. In this research, we will show an adaptive learning system that filter spam emails based on users action pattern as time goes by. In this paper, we consider relationship between users actions such as what action is took after one action and how long does it take. They analyze that each action has how much meaning, and that it has an effect on filtering spam emails. And that in turn determines weight for each email. In experimentation, we will compare results of system of this research and weighted Bayesian classifier using real email data set. Also, we will show how to handle personalization for concept drift and adaptive learning.

Ontology-driven visualization system for semantic searching

Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization.

Collaborative user tracking for community organization on blogosphere: a case study of eLearning@BlogGrid

The aim of this study is to recommend relevant information to users by organizing user communities on electronic learning environment. In this paper, we propose a weblog-based approach to modeling users during collaborative learning process. Thereby, we formulate user behaviors on blogosphere, e.g., posting articles, linking to neighbors, and interactions between neighbors. These user models are capable of being compared with others to quantify similarities between users. We apply co-occurrence analysis methods. In this study, we deploy BlogGrid platform to support information pushing service to students. Through our experimental results, we found out that average weighting measurement scheme with co-occurrence patterns from responding (e.g., comments and trackback) activities is the most significant patterns for information pushing on collaborative learning.

Link Strength-Based Collaborative Filtering for Enhancing Prediction Accuracy

User-based collaborative filtering recommends items to users by analyzing user preferences. Nearest neighbors are identified based on similarity between users and preference prediction of items is performed by using the nearest neighbors. The prediction accuracy depends on how the nearest neighbors are identified among users. In this paper, we propose link strength-based user modeling by applying trust information between users and item ratings to enhance the prediction accuracy. In the proposed user modeling, nearest neighbor candidate is extracted in traditional manner and final nearest neighbor is identified by calculating user ranking with trust information. Trust information between users is presented by link and consists of direct and indirect relation. We evaluate the prediction accuracy on recommended items and experimental results show that the prediction accuracy is improved by applying the proposed method.