Explainable online recommendation systems with self-identity theory and attribute learning method

In recent years, Online Shopping plays an important role in daily life and how to improve the online shopping experience with Machine Learning and Recommender System has been discussed by a group of researchers. As a sub-field of Machine Learning, Computer vision has achieved significant developments during the last decade. The computer vision techniques can help machine to view images and extract useful information from images like human beings. However, the existing online recommender system has mostly used the labelled information and ignored the large amount of useful information extracted from images. This thesis proposed that the extracted information from images through computer vision techniques can be used in the current online recommender system for the improved online shopping experience. To do this, I firstly tackled the problem of insufficient data in the real online shopping environment. I proposed a pairwise constraint random forest algorithm with associating transfer learning strategy. This new algorithm can make use of weakly supervised labelled data which is relatively easy to collect in the real online shopping environment to train the attribute classification model. Secondly, I developed an explainable recommender system with self-identity theory. This new recommender framework is built based on the weakly learning algorithm proposed above to analyse human behaviours by self-identity theory from information system research. Compared with previous recommender system, my work concentrates on different customer behaviours distinguished by self-identity and result in an improved online shopping experience. In summary, there are two major contributes for this thesis. Firstly, this thesis introduces a new weakly-supervised learning approach for semantic data classification in the online shopping environment. This new algorithm can work with noise partially labelled data to achieve better accuracy for attribute learning tasks. Secondly, by analysing the recommender system with self-identity theory, a new explainable Recommender System is proposed to improve online shopping experience. Besides, we also indicate the potential of further research in combining Computer Vision in Computer Science with online shopping experience in Information System research which can determine how Computer Vision can help to solve real world problems

The Design of an Online Roommate Finding System for Property Management Portals—the Virtual Community Perspective

With the evolution and diffusion of the internet at the society level, various business sectors have been transformed and redesigned with the new technologies such as enterprise systems, social media, virtual communities, social commerce, and business intelligence. For example, most fortune 500 companies have installed ERP system by 1998 (Magal & Word, 2012), resulting in major manufacturing and traditional businesses to re-design their business processes. The travel industry has been disrupted by the online booking system such as booking.com and TripAdvisor.com (Mellinas, Martínez María-Dolores, & Bernal García, 2015). Many bookshops have been closed because of the operations of online booking stores such as Amazon (Pitt, Berthon, & Berthon, 1999). The emerging smart city research also implies that the transformation by the digital technology is at the national and city level into all sectors including transportation, energy, water etc (Paroutis, Bennett, & Heracleous, 2014). In the property management sector, various property management portals have been established to facilitate the estate agencies to manage properties and the customers to find suitable properties. The typical examples are zoopla.com and rightmove.com, both of which are specialized in one-stop property renting and selling in the United Kingdom. At the same time, there are also many small property management agencies who operate the independent property management platforms. The traditional property management portal normally consists of three key functions, property listing and management for agencies; property browsing and appointment booking for customers; and the property information integration including neighbourhood, billing, local tax etc. While the traditional property management system has satisfied the great needs for estate agencies and focus heavily on the information of the properties themselves, the customer experience has mainly ignored from the traditional design of the website. Practically, there is a need to bring the most recent knowledge of improving online shopping experience into the property management system design. This paper proposed a new business model for property management portals by designing an online roommate finding system with the virtual community theory. Specifically, the virtual community concept is used to design the online roommate system from the interpersonal relationship perspective. The proposed online roommate finding system is designed based on the interpersonal relationship framework (Li & Lee, 2013) and borrowed the concept and algorithm of the online recommendation system (Jiang, Shang, & Liu, 2010). Specifically, the following functions are implemented for the online roommate finding system so that a renting community emerges from the rental portal, the profile information of each tenant, the desired roommate features including personality, age, country, native language, etc, and the inside communication channels. The proposed online roommate finding system is going to be integrated into the existing online property management portal in UK. The action research method will be used to test the effectiveness of the online roommate finding system from 3 stages for 3 years, stage one is for the system testing, stage 2 is for community member recruitment, stage 3 is for operations of the system for profit generation. The current project is in the early phase of stage 1 and the requirements analysis is finished

The concept and connotation of smart tourism from the perspective of rational choice

Although the term “smart tourism” originated from western countries, it has “taken root and flourished” in China. Current understanding in domestic industry and academia not only reflects the reality of the development of smart tourism in China, but also encourages new research into its conceptualization and strategy. Based on the understanding and analysis of the original mainstream technology application theory, this paper proposes new ideas on the concept and connotations of smart tourism from the perspective of rational choice theory. It concludes that the core characteristic of smart tourism is that it encourages the tourism subject to make the most rational choice

EventEA: Benchmarking Entity Alignment for Event-centric Knowledge Graphs

Entity alignment is to find identical entities in different knowledge graphs (KGs) that refer to the same real-world object. Embedding-based entity alignment techniques have been drawing a lot of attention recently because they can help solve the issue of symbolic heterogeneity in different KGs. However, in this paper, we show that the progress made in the past was due to biased and unchallenging evaluation. We highlight two major flaws in existing datasets that favor embedding-based entity alignment techniques, i.e., the isomorphic graph structures in relation triples and the weak heterogeneity in attribute triples. Towards a critical evaluation of embedding-based entity alignment methods, we construct a new dataset with heterogeneous relations and attributes based on event-centric KGs. We conduct extensive experiments to evaluate existing popular methods, and find that they fail to achieve promising performance. As a new approach to this difficult problem, we propose a time-aware literal encoder for entity alignment. The dataset and source code are publicly available to foster future research. Our work calls for more effective and practical embedding-based solutions to entity alignment.Comment: submitted to ISWC 202