Design And Implementation Of Dish Combo Recommendation Algorithm Based On User Reviews

With the rapid development of information technology and the Internet,a variety of products that people can buy online have increased dramatically.In the era of this online product explosion,mass data not only serves the people's daily life,but also brings great challenges to the two-way choice between the business and the users.In recent years,recommendation systems are widely used in network platforms.It can mine users' preferences to recommend appropriate products for users.The existing recommendation algorithms have achieved good results in the field of recommending a single product called item.However,With the rise of the new consumption pattern of Group,recommending item can not meet new consumption pattern in some specific industries.Application driven by a new consumption pattern,this paper studies the combo recommendation algorithm in the catering industry based on the data from Dianping.Given a clear definition of combo recommendation,this paper mainly studies the catering combo recommendation from the following three aspects:· Solve the Scarcity of Combo Data This paper makes semantic analysis of Group and non-Group data and calculates the similarity of them.It verifies the higher text similarity between Group and non-Group data from multiple angles.To a certain extent,a good solution that this paper proposes to expand the combo data with nonGroup data is used to solve the scarcity of combo data.· Combo Recommendation Based on Latent topic The algorithm analyzes the feature documents of users and combos to mine latent topic distribution of users and combos,and builds users' preferences and diversity characteristics of combos.This paper applies different regression models to analyze the relationship between the latent topics and user-combo ratings.The proposed algorithm can accurately calculate the user's predictive rating of the combo,and complete personalized combo recommendation.· Combo Recommendation Based on Potential Relevance By analyzing the rating data of each user,the algorithm builds the mapping between the dishes ratings and the hidden features to mine potential relevance between dishes and users and correlations between dishes themselves.Then combined with the collection of the combo,it can accurately calculate the prediction rating of the target user for the combo.Furthermore,the algorithm takes into account the implicit information between the user and the dishes to make the best use of the related information of combo data.This paper uses the implicit information to adjust the mapping between the dishes ratings and the hidden features,in order to mine potential relevance more accurately.