An improved model for trust-aware recommender systems based on multi-faceted trust

As customers enjoy the convenience of online shopping today, they face the problem of selecting from hundreds of thousands of products. Recommender systems, which make recommendations by matching products to customers based on the features of the products and the purchasing history of customers, are increasingly being incorporated into e-commerce websites. Collaborative filtering is a major approach to design algorithms for these systems. Much research has been directed toward enhancing the performance of recommender systems by considering various psychological and behavioural factors affecting the behaviour of users, e.g. trust and emotion. While e-commerce firms are keen to exploit information on social trust available on social networks to improve their services, conventional trust-aware collaborative filtering does not consider the multi-facets of social trust. In this research, we assume that a consumer tends to trust different people for recommendations on different types of product. For example, a user trusts a certain reviewer on popular items but may not place as much trust on the same reviewer on unpopular items. Furthermore, this thesis postulates that if we, as online shoppers, choose to establish trust on an individual while we ourselves are reviewing certain products, we value this individual’s opinions on these products and we most likely will value his/her opinions on similar products in future. Based on the above assumptions, this thesis proposes a new collaborative filtering algorithm for deriving multi-faceted trust based on trust establishment time. Experimental results based on historical data from Epinions show that the new algorithm can perform better in terms of accuracy when compared with conventional algorithms

Facilitating Technology Transfer by Patent Knowledge Graph

Technologies are one of the most important driving forces of our societal development and realizing the value of technologies heavily depends on the transfer of technologies. Given the importance of technologies and technology transfer, an increasingly large amount of money has been invested to encourage technological innovation and technology transfer worldwide. However, while numerous innovative technologies are invented, most of them remain latent and un-transferred. The comprehension of technical documents and the identification of appropriate technologies for given needs are challenging problems in technology transfer due to information asymmetry and information overload problems. There is a lack of common knowledge base that can reveal the technical details of technical documents and assist with the identification of suitable technologies. To bridge this gap, this research proposes to construct knowledge graph for facilitating technology transfer. A case study is conducted to show the construction of a patent knowledge graph and to illustrate its benefit to finding relevant patents, the most common and important form of technologies

A Dual-view Attention Neural Network for Assigning Industrial Categories to Academic Patents

Industrial technology matching events are held by governmental institutions worldwide to promote patent transfer from universities to industries. When collecting academic patents for the matching events, governmental institutions lack professional knowledge for identifying academic patents suitable for various industries. Therefore, previous studies adopted International Patent Classification (IPC) codes assigned by patent examiners to represent patents and mined the industry-related cues through the mapping link between IPC codes and industry categories. However, IPC codes are too general to specifically represent the complex patents, leading to inaccurate tagging. The view of patent inventors (e.g., patent titles and abstracts) contains rich industry-related cues that benefit assigning industrial categories to academic patents. Therefore, we propose a dual-view attention neural network that learns low-dimensional patent representations from the views of patent examiners and inventors and merges the representations for classifying academic patents into suitable industrial categories. Experiments show that the proposed method outperforms benchmark methods

Leveraging Trust Relations to Improve Academic Patent Recommendation

Academic patent trading is one of the important ways for university technology transfer. Compared to industry patent trading, academic patent trading suffers from a more serious information asymmetric problem. It needs a recommendation service to help companies identify academic patents that they want to pay. However, existing recommendation approaches have limitations in facilitating academic patent trading in online patent platforms because most of them only consider patent-level characteristics. A high trust degree of a company towards academic patents can alleviate the information asymmetry and encourage trading. This study proposes a novel academic patent recommendation approach with a hybrid strategy, combining citation-based relevance, connectivity, and trustworthiness. An offline experiment is conducted to evaluate the performance of the proposed recommendation approach. The results show that the proposed method performs better than the baseline methods in both accuracy and ranking

When Less is More: Effect of Group Size on Profit of Online Advertising Communities

As audience capacity is a fundamental index in ads campaigns, managers of online advertising communities always try to attract as many audiences as possible. However, due to the limited attention, effectiveness (click-through-rate) of advertisements is negatively influenced by the huge amount of information created by audiences in communities. At the same time, the great number of audiences in communities can generate greater social influence that may also affect the click-through-rate. Thus, it is necessary to figure out the optimal size of online communities. In this paper, we employ and model the effect of information congestion and social influence to solve the problem. According to the model, we find that more audiences do not mean more profit. At last, we point out some potential directions to improve the research in future, including proposing more realistic functions, relaxing control of posting position and time, and incorporating pricing strategy and communities’ competitions

Enhancing group recommendation by knowledge graph

With the rapid development of IT, more and more information/knowledge sharing and discovery activities are moved from offline to online and many online groups have been created to facilitate such activities. However, due to the information asymmetric and information overload problems, information/knowledge holders face difficulty disseminating their information/knowledge to online groups whose members are of interests. It is also difficult for groups of users to find the most related information/knowledge. Traditional individual recommendation techniques cannot solve this problem effectively because they cannot capture the preferences of a group of users. To generate recommendations for a group of users, this paper proposes a knowledge graph-enhanced group recommendation method in which knowledge graph is used to construct comprehensive profiles for groups and information/knowledge to be recommended. The proposed group recommendation method is evaluated with real- world data and the evaluation results demonstrate the effectiveness of the proposed method