Research Gap between Machine Learning and Online

Shopping Experience

The thesis, improving Online Shopping Experience by Knowledage discovery from communities, is a big topic include two popular research fields: Online Shopping Experience in Information System and Knowledge discovery from Computer Science. Both research areas are also include huge research topics. To clearify the connection between Online Shopping Experience and knowl- edge discovery and propose our research targets is our first work in the thesis. The target of research in Online Shopping Experience is to define and dis- cuss what factors in online shopping procedure can affect Customers’ shop- ping experience. In the review, we describe that the early researches in Infor-

mation System area are concentrate on high level overview of Online Shop- ping Experience which regard the Online Shopping Environment as a whole system or only spilt them into 4 or 5 parts (Frow & Payne, 2007; Grewal et al., 2009; H¨aubl & Trifts, 2000; Meyer & Schwager, 2007). With the de- veloping of online shopping, the components of Online Shopping Environ- ment has been treated as more detail particulars. To evaluate Online Shop- ping Experience, we can analyze the problem from a lots of subset of Online Shopping Environment such as Web Service (Kaynama & Black, 2000), Web Design (Dix, 2009; Zviran et al., 2006), Online Customer Behavior (Moor- man et al., 1992; Morgan & Hunt, 1994; Rousseau et al., 1998) and Online Recommendation System (Childers et al., 2002; Park & Kim, 2003). In this case, improving Online Shopping Experience can be regarded as a problem of improving a subfields listed above. By reviewing all those subfields, we conclude that Online Recommendation System is the most related field which can be improved by combining Information System theories and Knowledge discovery methods.

There are three reasons to focus on Online Recommendation System. Firstly, Online Shopping Recommendation System has been developed in both Information System and Computer Science area, but the research tar- get is different. The research in Information System tries to analyze hu- man response to the RS and how RS helps in Online Shopping Environ- ment (Childers et al., 2002; Park & Kim, 2003). On the other hand, re- searchers in Computer Science mostly focus on the performance and how new elements from websites can be used in RS (H. Chen et al., 2012a; Di et al., 2013a). Secondly, our research methods, knowledge discovery from com- munities, need huge data to analyze and produce results. Coincidentally, the online shopping recommendation system is built based on those large amount of data which are a good resource for our knowledge discovery (Hu et al., 2015a). Finally, there is a research gap in Online Recommendation System between Information System and Computer Science. The RS in Information System is usually treated as a black box, researchers do not want to consider how RS works and what the prediction procedure means in Online Shopping Experience. The researches in Computer Science area, on the contrary, are all data centered that they only interest in mathematics or algorithms in RS, the feedback from customers is rarely considered (G.-G. Lee & Lin, 2005). In conclusion, to fill the research gap which investigate RS as a white box and improve RS with the results from Information System becomes our target.

Knowledge discovery, as our research methods, is another big topic in Computer Science. The sub-fields of knowledge discovery include Data Min- ing, Big Data analysis, Information Retrial and Machine Learning. In Chap- ter 2, we discussed the recent research trend (Lecun et al., 2015) in Machine Learning and how Machine Learning helps RS Iwata et al. (2011). We partic- ular focus on Computer Vision, a sub-field of Machine Learning, to discuss how it helps RS in Information System view. The reason to choose Computer Vision is in two-fold. On the one hand, Computer Vision attracted consid- erable attentions due to the developing of Deep Learning and it provides the ability to analyzing complex image data which can help to understand the RS (Lecun et al., 2015). The Computer Vision provides the potential to split Online Shopping Experience into more detail parts. On the other hand, RS is built based on a large amount of data which is a good resource for Com- puter Vision. Actually, there are already a lot of research in improving RS with image features, but most those research are built on well picked data rather than real online shopping environment (H. Chen et al., 2012a; Di et al., 2013a). Therefore, how Computer Vision can help Online Shopping Expe- rience, especially Online Recommendation System, and how it work in real Online Shopping Environment becomes another research target.

Here we propose our research target in both Computer Science and Infor- mation System field:

Computer Science The Computer Vision methods are mostly developed on well picked data, but real Online Shopping Environment contains lots of noisy and unlabeled data. So the weekly supervised learning problem of extending current Computer Vision methods to handle noisy and par- tially labeled data in online shopping environment is our first research target.

Information Science Most research in Information System regards RS as a black box which do not analyze the inside the recommend approach. The second target is to introduce a theory to describe RS behavior and improve RS with Information System theories.

Those two research targets are introduced based on reviewing related work and try to fill the research gap between Online Shopping Experience and Computer Vision. The result of our research could improve Online Shopping Experience in both Computer Science and Information System fields.