Huizhi Liang

Collaborative Filtering Recommender Systems Using Tag Information

Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviors such as purchase behavior, click streams, and browsing history etc., the tagging information implies userpsilas important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data.

Parallel User Profiling Based on Folksonomy for Large Scaled Recommender Systems: An Implimentation of Cascading MapReduce

The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users¡¯ interests and preferences to make personalized recommendations. To solve the scalability problem of the current user profiling and recommender systems, this paper proposes a parallel user profiling approach and a scalable recommender system. The current advanced cloud computing techniques including Hadoop, MapReduce and Cascading are employed to implement the proposed approaches. The experiments were conducted on Amazon EC2 Elastic MapReduce and S3 with a real world large scaled dataset from Delicious website.

Developing Trust Networks Based on User Tagging Information for Recommendation Making

Recommender systems are one of the recent inventions to deal with ever growing information overload. Collaborative filtering seems to be the most popular technique in recommender systems. With sufficient background information of item ratings, its performance is promising enough. But research shows that it performs very poor in a cold start situation where previous rating data is sparse. As an alternative, trust can be used for neighbor formation to generate automated recommendation. User assigned explicit trust rating such as how much they trust each other is used for this purpose. However, reliable explicit trust data is not always available. In this paper we propose a new method of developing trust networks based on users interest similarity in the absence of explicit trust data. To identify the interest similarity, we have used users personalized tagging information. This trust network can be used to find the neighbors to make automated recommendations. Our experiment result shows that the proposed trust based method outperforms the traditional collaborative filtering approach which uses users rating data. Its performance improves even further when we utilize trust propagation techniques to broaden the range of neighborhood.

Dynamic Similarity-Aware Inverted Indexing for Real-Time Entity Resolution

Entity resolution is the process of identifying groups of records in a single or multiple data sources that represent the same real-world entity. It is an important tool in data de-duplication, in linking records across databases, and in matching query records against a database of existing entities. Most existing entity resolution techniques complete the resolution process offline and on static databases. However, real-world databases are often dynamic, and increasingly organizations need to resolve entities in real-time. Thus, there is a need for new techniques that facilitate working with dynamic databases in real-time. In this paper, we propose a dynamic similarity-aware inverted indexing technique (DySimII) that meets these requirements. We also propose a frequency-filtered indexing technique where only the most frequent attribute values are indexed. We experimentally evaluate our techniques on a large real-world voter database. The results show that when the index size grows no appreciable increase is found in the average record insertion time (around 0.1 msec) and in the average query time (less than 0.1 sec). We also find that applying the frequency-filtered approach reduces the index size with only a slight drop in recall.

Personalized recommender system based on item taxonomy and folksonomy

Item folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. In this paper, we propose to combine item taxonomy and folksonomy to reduce the noise of tags and make personalized item recommendations. The experiments conducted on the dataset collected from Amazon.com demonstrated the effectiveness of the proposed approaches. The results suggested that the recommendation accuracy can be further improved if we consider the viewpoints and the vocabularies of both experts and users.

Tag Based Collaborative Filtering for Recommender Systems

Collaborative tagging can help users organize, share and retrieve information in an easy and quick way. For the collaborative tagging information implies users important personal preference information, it can be used to recommend personalized items to users. This paper proposes a novel tag-based collaborative filtering approach for recommending personalized items to users of online communities that are equipped with tagging facilities. Based on the distinctive three dimensional relationships among users, tags and items, a new similarity measure method is proposed to generate the neighborhood of users with similar tagging behavior instead of similar implicit ratings. The promising experiment result shows that by using the tagging information the proposed approach outperforms the standard user and item based collaborative filtering approaches.

Noise-Tolerant Approximate Blocking for Dynamic Real-Time Entity Resolution

Entity resolution is the process of identifying records in one or multiple data sources that represent the same real-world entity. This process needs to deal with noisy data that contain for example wrong pronunciation or spelling errors. Many real world applications require rapid responses for entity queries on dynamic datasets. This brings challenges to existing approaches which are mainly aimed at the batch matching of records in static data. Locality sensitive hashing (LSH) is an approximate blocking approach that hashes objects within a certain distance into the same block with high probability. How to make approximate blocking approaches scalable to large datasets and effective for entity resolution in real-time remains an open question. Targeting this problem, we propose a noise-tolerant approximate blocking approach to index records based on their distance ranges using LSH and sorting trees within large sized hash blocks. Experiments conducted on both synthetic and real-world datasets show the effectiveness of the proposed approach.

Mining Users' Opinions Based on Item Folksonomy and Taxonomy for Personalized Recommender Systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users¡¯ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users¡¯ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

Dynamic Sorted Neighborhood Indexing for Real-Time Entity Resolution

Real-time entity resolution is the process of matching query records in sub-second time with records in a database that represent the same real-world entity. Indexing techniques are used to efficiently extract a set of candidate records from the database that are similar to a query record, and that are then compared with the query record in more details. The sorted neighborhood indexing method, which sorts a database and compares records within a sliding window, has successfully been used for entity resolution of very large databases. However, because it is based on static sorted arrays, this technique is not suitable for dynamic databases. We propose a tree-based dynamic sorted neighborhood index that facilitates matching a stream of query records against a large and dynamic database in real-time. We evaluate our approach on two large data sets. Our results show that the times for both inserting and querying of records stays nearly constant as the index grows, and our approach achieves over one magnitude faster indexing and querying times compared to an earlier real-time entity resolution technique with comparable high matching accuracy.