Users’ preferences, and consequently their ratings and reviews to items, change over time. Likewise, characteristics of items are also time-varying. By dividing data into time periods, temporal Recommender Systems (RSs) improve recommendation accuracy by exploring the temporal dynamics in user rating data. However, temporal RSs have to cope with rating sparsity in each time period. Meanwhile, reviews generated by users contain rich information about their preferences, which can be exploited to address rating sparsity and further improve the performance of temporal RSs. In this article, we develop a temporal rating model with topics that jointly mines the temporal dynamics of both user-item ratings and reviews. Studying temporal drifts in reviews helps us understand item rating evolutions and user interest changes over time. Our model also automatically splits the review text in each time period into interim words and intrinsic words. By linking interim words and intrinsic words to short-term and long-term item features, respectively, we jointly mine the temporal changes in user and item latent features together with the associated review text in a single learning stage. Through experiments on 28 real-world datasets collected from Amazon , we show that the rating prediction accuracy of our model significantly outperforms the existing state-of-art RS models. And our model can automatically identify representative interim words in each time period as well as intrinsic words across all time periods. This can be very useful in understanding the time evolution of users’ preferences and items’ characteristics.

The open and anonymous nature of peer-to-peer (P2P) networks makes it an ideal medium for attackers to spread malicious contents, which in turn leads to lower quality of network services due to lack of effective trust management mechanism. To improve the quality of services (or transactions), this paper proposes a novel trust and reputation model, named as GroupTrust, based on peer group and evaluation similarity degree in P2P networks. In the proposed model, trust relationships between peers are divided into three categories: trust relationship within a peer group, trust relationship between different groups, and trust relationship between a peer in a peer group with another peer out of this peer group. The model presents the evaluation similarity degree under different context of services and gives local and global reputation computation. Experimental results demonstrate that this model can get more real trust value and deal with the malicious attacks efficiently by comparison with existing models. © 2010 Wiley Periodicals, Inc.

With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly. In this paper, we study the effectiveness and the efficiency issues of answering top-k keyword query in relational database systems. We propose a new ranking formula by adapting existing IR techniques based on a natural notion of virtual document. We also propose several efficient query processing methods for the new ranking method. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimental results demonstrate significant improvement to the alternative approaches in terms of retrieval effectiveness and efficiency.

In this paper, we address the problem of cache replacement for transcoding proxy caching. First, an efficient cache replacement algorithm is proposed. Our algorithm considers both the aggregate effect of caching multiple versions of the same multimedia object and cache consistency. Second, a complexity analysis is presented to show the efficiency of our algorithm. Finally, some preliminary simulation experiments are conducted to compare the performance of our algorithm with some existing algorithms. The results show that our algorithm outperforms others in terms of the various performance metrics.

Web caching is an important technique for reducing web traffic, user access latency, and server load and cache replacement plays an important role in the functionality of web caching. In this paper we propose an improved GreedyDual (GD) cache document replacement algorithm, which considers update frequency as a factor in its utility function. We use both trace data and statistical data to simulate our proposed algorithm. The experimental results show that our improved GD algorithm can outperform the existing GD algorithm over the performance metrics considered.