Comparison of common algorithms for recommender systems

In the recommendation system Introduction, we give the general framework of the recommendation system. Obviously, the recommendation method is the most important part of the recommendation system, which determines the performance of the recommended system to a large extent. At present, the main recommended methods include: Based on content recommendation, collaborative filtering recommendation, based on association rule recommendation, based on utility recommendation, based on knowledge recommendation and combination recommendation.

First, based on content recommendation

Content-based recommendation (content-based recommendation) is the continuation and development of information filtering technology, which is based on the content information of the project to make recommendations, and does not need to be based on the user's evaluation of the project, more need to use the machine The method of learning is to obtain the user's interest information from the case of the characteristic description of the content. In the content-based recommender system, the project or object is defined by the attributes of the relevant characteristics, and the system is based on the characteristics of the user's evaluation object, learns the user's interest, and examines the matching degree between the user data and the project to be predicted. The user's data model depends on the learning method used, the decision tree, the neural network and the vector-based representation method. Content-based user data is a historical data that requires a user, and the user profile model may change as the user's preferences change.

Second, collaborative filtering recommendations

Collaborative filtering recommendation (collaborative Filtering recommendation) technology is one of the earliest and most successful technologies used in recommender systems. It generally uses the nearest neighbor technology, uses the user's historical preferences information to calculate the distance between users, and then uses the target user's nearest neighbor user to the commodity appraisal value to predict the target user to the specific product preference degree, the system thus according to this preference degree to the target user to recommend. The biggest advantage of collaborative filtering is that there is no special requirement for the recommended object, which can handle unstructured complex objects, such as music and movies.

III. recommendation based on association rules

The recommendation based on Association Rules (Association rule-based recommendation) is based on the association rules, the purchased goods as the rule header, the rule body as the recommended object. Association rules mining can find the correlation of different goods in the sales process, and has been successfully applied in the retail industry. The rule of management is to count the percentage of transactions in a transaction database that have purchased a trade set X and purchase a commodity set Y, and the intuitive meaning is how much the user is inclined to buy something else when they buy some. For example, when buying milk, many people buy bread at the same time.

Iv. recommendation based on utility

The utility-based recommendation (utility-based recommendation) is built on the utility of the user's use of the project, and the core question is how to create a utility function for each user, so the user data model is large Degree is determined by the utility function used by the system. The benefit of a utility recommendation is that it takes into account utility calculations for non-product attributes, such as the reliability of the provider (Vendor reliability) and the availability of products (product availability).

V. Based on Knowledge recommendation

A knowledge-based recommendation (knowledge-based recommendation) can be seen in some way as a reasoning (inference) technique that is not recommended based on user needs and preferences. Knowledge-based methods differ significantly from the functional knowledge they use. Utility knowledge (functional knowledge) is a kind of knowledge about how a project satisfies a particular user, so it can explain the relationship between need and recommendation, so the user data can be any knowledge structure that can support inference, it can be the query that the user has normalized, It can also be a more detailed representation of the user's needs.

Six, the combination of recommendations

Because of the pros and cons of various recommended methods, in practice, combinatorial recommendations (Hybrid recommendation) are often used. The most researched and applied combination of content recommendations and collaborative filtering recommendations. The simplest approach is to use a content-based approach and a collaborative filtering recommendation method to produce a recommended prediction result, and then combine the results with a method. Although there are many recommended combinations in theory, they are not always effective in a specific problem, and the most important principle of combinatorial recommendation is that the weaknesses of the recommended technologies can be avoided or remedied by the combination.

Comparison of common algorithms for recommender systems