Hybrid based Collaborative Filtering with Temporal Dynamics

Hybrid-based collaborative filters use some part or entire database relating to user preferences for making recommendations for new products and new users. In our time, it is of utmost importance to make recommendations in line with interests and demands of users by making their interest alive. However, although Hybrid-based collaborative filters are used in this area, changing of preferences of users in a time, emergence of new products and new users overshadow success of such systems. Traditional hybrid-based collaborative filtering (CF) technique become insufficient for responding interests and demands changing in a time. For this reason, temporal changes in recommendation systems become an important concept. Together with the study conducted, an appropriate and new method has been developed in line with changing pleasure and demands depending on time. In the recommended system, unlike traditional hybrid technique based CF technique, point given to the products depending on dates scored by users has been attempted to be estimated. In this study, process has been made over netflix data for measuring success of both traditional hybrid based CF technique and the recommended system. Quite successful and rewarding results have been obtained in the issue of accuracy of predicted points. Keywords- Recommendation System;, Data Mining; Temporal Dynamics

Recommendation of Multimedia Objects Using Metadata and Link Analysis

We investigate methods for recommending multimedia items suitable for an online multimedia sharing community and introduce a novel algorithm called UserRank for ranking multimedia items based on link analysis. We also take the initiative of applying EigenRumor from the domain of blogosphere to multimedia. Furthermore, we present a strategy for making personalized recommendation that combines UserRank with collaborative filtering. We evaluate our method with an informal user study and show that results obtained are promising

Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting

Recommender systems (RS) analyze user rating information and recommend items that may interest users. Item-based collaborative filtering (IBCF) is widely used in RSs. However, traditional IBCF often cannot provide recommendations with good predictive and classification accuracy at the same time because it assigns equal weights to all items when computing similarity and prediction. However, some items are more relevant and should be assigned greater weight. To address this problem, we propose a niche approach to realize item-variance weighting in IBCF in this paper. In the proposed approach, to improve the predictive accuracy, a novel time-related correlation degree is proposed and applied to form time-aware similarity computation, which can estimate the relationship between two items and reduce the weight of the item rated over a long period. Furthermore, a covering-based rating prediction is proposed to increase classification accuracy, which combines the relationship between items and the target user’s preference into the predicted rating scores. Experimental results suggest that the proposed approach outperforms traditional IBCF and other existing work and can provide recommendations with satisfactory predictive and classification accuracy simultaneously.

A Robust Collaborative Filtering Approach Based on User Relationships for Recommendation Systems

Personalized recommendation systems have been widely used as an effective way to deal with information overload. The common approach in the systems, item-based collaborative filtering (CF), has been identified to be vulnerable to “Shilling” attack. To improve the robustness of item-based CF, the authors propose a novel CF approach based on the mostly used relationships between users. In the paper, three most commonly used relationships between users are analyzed and applied to construct several user models at first. The DBSCAN clustering is then utilized to select the valid user model in accordance with how the models benefit detecting spam users. The selected model is used to detect spam user group. Finally, a detection-based CF method is proposed for the calculation of item-item similarities and rating prediction, by setting different weights for suspicious spam users and normal users. The experimental results demonstrate that the proposed approach provides a better robustness than the typical item-based kNN (k Nearest Neighbor) CF approach

A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented

Predicting Repository Upkeep with Textual Personality Analysis

GitHub is an excellent democratic source of software. Unlike traditional work groups however, GitHub repositories are primarily anonymous and virtual. Traditional strategies for improving the productivity of a work group often include external consultation agencies that do in-person interviews. The resulting data from these interviews are then reviewed and their recommendations provided. This is one such claim of a group of strategies called group dynamics. In the online world however where colleagues are often anonymous and geographically dispersed, it is often impossible to apply such approaches. We developed experimental methods to discern the same information that one would normally obtain through in-person interviews through automated means. Here we provide this automated method of data collection and analysis that can later be applied for the purposes of recommendation agents. Comments from individual developers were collected via various GitHub APIs. That data was then converted into personality traits for each individual through textual persona extraction and mapped to a personality space called SYMLOG. The resulting dynamics between each of the personalities of the developers of each repository are analyzed though SYMLOG to predict how successful each project is likely to be. These predictions are compared against valid preexisting success metrics

Using contextual and social links in information retrieval

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Search using social networks

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 59-61).In this thesis, we present an approach to the problem of personalized web search which makes use of the searcher's social network, in addition to the hyper-link based score used in most search engines. This combination of social and absolute search scores aims to improve the visibility of information that is relevant to the searcher, while maintaining any absolute measures of document importance . In our approach we adopt a flexible framework for combining information from different sources using Rank Aggregation techniques. Our search system, implemented using Java and Python, covers all the events and web pages present on MIT owned websites. We discuss the theory, design,and implementation of this system in details.by Ammar Ammar.M.Eng