The Pros and Cons of Mining User Data to Provide Personalized Recommendations

Today, consumers have high standards to decide which products meet their needs. Therefore, a satisfying shopping experience is defined as an experience that consumers can quickly find what they are looking for. E-commerce or e-commerce solves these high standards, enables consumers to enter search criteria, narrows inventory of online retailers to the products they are looking for, then orders the comfortable home put out. However, online retailers need to quickly match consumers with the products they want, and if the customer thinks that the search is not going well, they will just let the online retailers organize them.

Yidian Zixun is an interest-oriented mobile news aggregator that provides search and recommendation technology to provide users with personalized content. The company builds user profiles based on big data and other technologies. Collect professionals to generate content and distribute that content to users based on profiles of interest. In addition, that content attracts a large number of users and rewards profitable professionals. In addition, Yidian Zixun has built its own marketing database based on user's interest, which makes it more accurate to match users and advertisements.

A content-based recommender works with user-specified data or a clear movie rating of the MovieLens data set. Based on this data, a user profile is generated and used to make recommendations to users. As users provide more input and act on recommendations, the engine becomes increasingly accurate. TF is simply the frequency of words in a document. IDF is the reciprocal of the frequency of documents in the entire document set. There are two main reasons for using TF-IDF. Suppose you search for "results of the latest European football" on Google. Certainly, "the" occurs more frequently than "soccer games", but the relative importance of football games is higher than that of search queries. In this case, the weighting of the TF-IDF cancels the influence of the high-frequency word in judging the importance of the project (document).

User Behavior Data This is the data collected based on the specific operation performed by each user who accessed the site. That is the sweetest message. It provides detailed user data, mainly used for personalization. Rather than listing all possible variables, list some of the main variables. These data can tell you what your customers most care about, their special preferences and so on. I will explain the use case below. Although this is not the rarest data, it is definitely the most valuable and unique information to distinguish your personalized products from other markets.

Personalization of travel and hotel customer experience using behavioral analysis and machine learning