Xueping Peng

Fine-grained Product Features Extraction and Categorization in Reviews Opinion Mining

With the growth of user-generated contents on the Web, product reviews opinion mining increasingly becomes a research practice of great value to e-commerce, search and recommendation. Unfortunately, the number of reviews is rising up to hundreds or even thousands, especially for some popular items, which makes it a laborious work for the potential buyers and the manufacturers to read through them to make a wise decision. Besides, the free format and the uncertainty of reviews expressions, make fine-grained product features extraction and categorization a more difficult task than traditional information extraction techniques. In this work, we propose to treat product feature extraction as a sequence labeling task and employ a discriminative learning model using Conditional Random Fields (CRFs) to tackle it. We innovatively incorporate the part-of-speech features and the sentence structure features into the CRFs learning process. For product feature categorization, we introduce the semantic knowledge-based and distributional context-based similarity measures to calculate the similarities between product feature expressions, then an effective graph pruning based categorizing algorithm is proposed to classify the collection of feature expressions into different semantic groups. The empirical studies have proved the effectiveness and efficiency of our approaches compared with other counterpart methods.

Personalized web search using clickthrough data and web page rating

Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to construct personalized information retrieval model from the users’ clickthrough data and Web page ratings. This model builds on the user-based collaborative filtering technology and the top-N resource recommending algorithm, which consists of three parts: user profile, user-based collaborative filtering, and the personalized search model. Firstly, we conduct user’s preference score to construct the user profile from clicked sequence score and Web page rating. Then it attains similar users with a given user by user-based collaborative filtering algorithm and calculates the recommendable Web page scoring value. Finally, personalized informaion retrieval be modeled by three case applies (rating information for the user himself; at least rating information by similar users; not make use of any rating information). Experimental results indicate that our technique significantly improves the search performance.

News topic detection based on hierarchical clustering and named entity

News topic detection is the process of organizing news story collections and real-time news/broadcast streams into news topics. While unlike the traditional text analysis, it is a process of incremental clustering, and generally divided into retrospective topic detection and online topic detection. This paper considers the feature changes of modern news data experienced from the past, and presents a new topic detection strategy based on hierarchical clustering and named entities. Topic detection process is also divided into retrospective and online steps, and named entities in the news stories are employed in the topic clustering algorithm. For the online steps efficiency and precision, this paper first clusters news stories in each time window into micro-clusters, and then extracts three representation vectors for each micro-cluster to calculate the similarity to existing topics. The experimental results show remarkable improvement compared with recently most applied topic detection method.

Mining Web Access Log for the Personalization Recommendation

This paper presents a personalization recommendation model to recommend potentially interesting resources to users based on the Web access log of users. This model builds on the apriori algorithm and the tf-idf technology, which consists of three parts: resource description, users preference extraction and the personalization recommendation. Firstly, our model generates resource text space vector by analyzing the resource information achieved by mining users Web access log, then it attains interest set to make use of the apriori algorithm based on the vector, finally, it recommends filtered and sorted resources to users content based recommendation model.

Discovering Sequential Rental Patterns by Fleet Tracking

As one of the most well-known methods on customer analysis, sequential pattern mining generally focuses on customer business transactions to discover their behaviors. However in the real-world rental industry, behaviors are usually linked to other factors in terms of actual equipment circumstance. Fleet tracking factors, such as location and usage, have been widely considered as important features to improve work performance and predict customer preferences. In this paper, we propose an innovative sequential pattern mining method to discover rental patterns by combining business transactions with the fleet tracking factors. A novel sequential pattern mining framework is designed to detect the effective items by utilizing both business transactions and fleet tracking information. Experimental results on real datasets testify the effectiveness of our approach.

Query Expansion Based on Query Log and Small World Characteristic

Automatic query expansion is an effective way to solve the word mismatching and short query problems. This paper presents a novel approach to Expand Queries Based on User log and Small world characteristic of the document (QEBUS). When the query is submitted, the synonymic concept of the query is gotten by searching a synonymic concept dictionary. Then the query log is explored and the key words are extracted from the user clicked documents based on small world network (SWN) characteristic. By analyzing the semantic network of the document based on SWN and exploring the correlations between the key words and the queries based on mutual information, high-quality expansion terms can be gotten. The experiment results show that our technique outperforms some traditional query expansion methods significantly.