International Core Journal of Engineering 2020-26 | Page 127

recommendation system and discuss the requirements and objectives for cellphone recommendation. structural user choice model (SUCM) to cope with apps in a hierarchical taxonomy [1]. They modeled the user choices as two phases, in the first phase, a user decides which type of apps to choose. In the second phase, the user chooses apps in the selected category [1]. However, for some complicated classifications that cannot be split into only two phases, the method becomes complex and difficult to implement [1]. A. Importance of Cellphone Recommendation We find out that with all kinds of cellphones in the cellphone market, consumers are perplexed by the markets and do not know how to choose a cellphone. For instance, they are unlikely to know about the functionalities and performance of a cellphone, and whether they are comfortable with the operating systems. Also, they do not know how to satisfy their requirements on cellphone without wasting much money. Besides, consumers need a visualized evaluation of specific aspects like photography, business use to choose from the large scope of market cellphones. IV. O UR P ROPOSED M ETHOD Large-scale technology companies like Huawei and Xiaomi have investigation groups to decide what features and characteristics should be equipped in the next generation of their cellphones [4][12]. For example, several corporations conduct requirement analysis and make modifications based on the last generation of their products [13]. They may modify rear cameras to achieve better photographing or hide the front camera under the screen [14]. Our proposed method is designed to satisfy consumers in assisting them to choose from these techniques and facilities, and offer them a suitable cellphone. However, there are some challenges: 1) the characteristics of every consumer are highly different, and it is difficult to model the unique traits using a universal method; 2) some consumers are unfamiliar with cellphones, and thus unable to give any information about cellphone preference; 3) whether a consumer will make a purchase depends on a complex set of factors. As such, we define three research questions: 1) what are the factors and features that impact a cellphone purchase? 2) how to build a connection between target users’ information and specific cellphones? 3) how to model the set of factors and features to represent portraits of every consumer? 4) how to develop the model to correctly recommend the cellphone with the highest purchase rate and customer satisfaction rate. Consequently, we propose a cellphone recommendation system so as to help consumers choose the most appropriate cellphones and create the most satisfactory cellphone using experience. In consideration of the popularity of cellphones, there are a huge amount of target consumers. As such, the cellphone recommendation system should be researched on. B. Business Requirement and Objectives for Cellphone Recommendation It is surveyed that there are few functionally-similar products in the market. Hence, the system possesses a high market share, as well as indirectly increase the overall profits from cellphone sales, either by increasing the number of consumers or by increasing the willingess for each consumer to buy the suitable cellphone recommended by the system. Moreover, the requirement also aims to give those cellphone businesses a larger exposure to the market [7]. In this paper, we mainly aim at the objectives of increasing the cellphone purchase rate and the customer satisfaction rate. Our proposed method, CIMR, answers all the four research questions defined above. When applied within the surveyed consumers in an offline way, CIMR is able to fulfill all the requirements and objectives answered by consumers. III. R ELATED W ORK Machine learning techniques are widely applied on recommender systems and on predicting users’ preferences [8][9][10][11], and we discuss a few of them in this section. The cellphone recommendation issue stated in this paper highly resembles several prediction and decision-making problems. A. An Overview of CIMR The overview of CIMR is shown in the follow as Fig. 1. A. Personalized Trip Recommendation Based on Users’ information Researchers from Melbourne University proposed a point of interest (POI) recommendation mechanism for tourists based on user interests, visit duration and visit recency in 2017 [8]. It addressed the problems for tourists to choose POIs of an unfamiliar city considering their own interests [8]. The proposed method formulated the tour recommendation problems based on the Orienteering problem, which considered a user’s trip constraints such as time limitations and the needs for the tour to start and end at specific POIs [8]. There are several drawbacks with this mechanism: first, it didn’t separate similar POIs to each other, which would lead to random allocations of those similar POIs; second, user interests can be conflicted with POI popularity, and may not come up with a more precise result. In addition, the POIs lacked enough features to build connections with users. Fig. 1. Overview of CIMR The method consists of seven procedures: 1) we obtain surveyed data about users’ personal situation(i.e. hobbies and cellphone usage) and their cellphones; 2) we obtain target consumers who are willing to buy a new cellphone; 3) we then carry out data preprocessing, splitting users’ data based one different cellphones; 4) we use the survey data to train machine learning models to predict the purchase rate (probability) of a particular consumer; 5) we calculate the purchase rate for various cellphones; 6) targeted at different cellphone, we compare the corresponding predicted purchase B. Mobile App Recommendation The researchers from IBM research center proposed a 105