The increasing development of mobile and the supported applications having a vital role in user’s life due to simplicity, bearable price, and extensive features that are needed in the daily routine of many circumstances. The millions of apps are available to the users in the app store and the problem is how to deliver the specific app which the user desires. Most of the app stores suggest apps to the users either by no. of downloads or the comments of the user on that app. From one perspective, it makes a way for rising fraudulent apps by faking downloads and comments and on the other hand, the apps that are not desirable to the user are entered into the list. It is mandatory to design an RS that has to recommend the apps to the users based on their interest and should vary from time to time. The better RS not only favors the user but also the platform where the apps are laid out. More number of installs by the user in the platform increases the revenue and profit if the chosen RS is effective.
The parameters involved in RS are quite a lot. At the known level, RS comprises three entities: a user categorizer, a rank regenerator, and a framework to satisfy user needs. The user categorizer is based on a deep retrieval model that can analyze the apps in store among millions and recommend the better ones. For each app, a rank regenerator, i.e. a user tracking and preference model, predicts the user’s activities along multiple dimensions with the help of parameters like downloaded apps, app usage time, browsing time over app and category, etc. Then make them as input to a Multi-target streamlining model whose output gives the most reasonable recommendations to the user.
App development has become one of the platforms for generating revenue and earning profits. Some free or low-graded apps were introduced in the store to raise the revenue of developers. As downloads have been increased, it doesn’t matter whether it works or satisfies the user. This type of one-trick applications is easily reachable to user due to the lack of visibility of apps that offers better service to achieve the objectives of the intended user. If the user’s interests matched with the app characteristics through memory-based and model-based recommendation systems, the visibility of the genuine apps has been increased. Due to the dynamic nature of the model, the updates and the new arrivals influence the model only if it holds the primary features to attain the objectives of user preference [1].
App popularity plays a key role in app recommendation as it is based on rating, ranking, and reviews. Most recommendation systems follow this popularity attribute to match the user preferences to recommend apps. As it varies and sub-attributes are heterogeneous, popularity-based HMM has been used with trend-based recommendation systems. It calculates the rank γrank and average rating of the user γrate. On averaging the values with fusion factor α, a popularity score can be derived that not only is used to recommend apps but also indicate the apps trend in the future [2].
Most of the recommendation systems concentrate on user preference and app context. Though the role of the above attribute is big and dynamic, the relationship between the app and reviews has been not considered fully. Instead of deciding sentiments on review, evolve the process to determine the review similarity. As this process is iterative, the converging point of the process determines the relationship between the apps. If it didn’t converge, the recommendation set can be revised with the above-obtained results. That not only produce better recommendation apps but is also used to calculate the relationship between the apps [3].
The rest of this paper is organized as follows. In Section 2, the evolution of app store recommendation techniques has been discussed. Section 3 presents the framework of a personalized recommendation system. Section 4 depicts the process flow of CNN based Topic Recommendation System (CTRS). Experiment results and analysis are shown in Section 5. The conclusions and further enhanced are given in Section 6.