Author: Zen and the Art of Computer Programming
1 Introduction
The recommendation system is a very important and shocking data mining methodology, which is widely used in many fields such as e-commerce, mobile Internet, social networks, and games. Due to the emergence of various emerging network economic forms and the explosive growth of user behavior data on e-commerce platforms, product recommendation systems based on demographic data and product purchasing habit analysis are almost impossible to implement. How to effectively extract valuable information from massive amounts of information has become a hot topic in current AI learning. With the rapid development of artificial intelligence technology, recommendation systems have also ushered in an era of vigorous development. In this article, we will focus on the application of recommendation systems in e-commerce, introduce the basic concepts, basic knowledge, recommendation algorithms and related research progress of recommendation systems, and use practical examples to demonstrate the application of recommendation systems in e-commerce. Finally, we will also provide an outlook on future challenges.
2. Recommendation system concepts and related definitions
(1) What is a recommendation system?
Recommendation System, also known as "collaborative filtering" or "content-based recommendation", refers to analyzing the user's potential preferences based on the user's past historical behavior, preferences, interests and other information, and providing users with similar interests and hobbies Personalized recommendations for products or services. Recommender systems are usually divided into two types: static and dynamic. The static type recommendation system has completed the recommendation process in the modeling stage, that is to say, the system makes recommendations based on certain specific rules, such as category labels of goods, relationships between items, etc. However, this system often has shortcomings, mainly reflected in the following aspects:
- For a long period of time, the system cannot accurately reflect the real needs of users;
- Users’ interests change relatively slowly, and the system cannot adjust recommendation results in a timely manner;
- Failure to take into account the long-tail distribution of users towards the product;
- Not adaptable to new or unpopular products.
The dynamic type of recommendation system is a real-time, user-based recommendation system that can quickly respond to user feedback and adjustments. It analyzes the user's behavior logs, browsing records, search queries and other data, combined with contextual environment and historical behavior habits. Use data mining, machine learning and other technologies to generate new recommendation results and make accurate recommendations.
(2) Classification of recommendation systems
Currently, recommendation systems can be divided into the following three broad categories:
User-based Recommendation Systems. As the name suggests, this type of system is user-centered and uses information such as interactive behavior and preference differences between users to make recommendations. These systems usually adopt a collaborative filtering model based on items (Items), which first clusters based on users' historical behavior records, and then uses the similarities among user groups and the similarities between different items to provide personalized services for each user. recommendation.
Item-based Recommendation Systems. Similar to the former, this type of system centers on items (Items), directly analyzes the user's historical behavior, preferences and other information, and recommends other items that are similar or related to the target item to the user. The difference is that this system first needs to build a product map and make recommendations based on the similarities between products.
Mixed-type Recommendation Systems. This system combines the advantages of the two systems mentioned above. It not only takes into account the correlation between users and items, but also provides solutions to some shortcomings. Hybrid recommendation systems can combine two models, such as using both user-based and item-based collaborative filtering models.
(3) Evaluation criteria for recommendation systems
There are many ways to evaluate recommendation systems, including accuracy, recall, coverage, diversity, novelty, SERP indicators, etc. Generally speaking, precision, recall and diversity are key indicators for measuring recommendation systems. Among them, accuracy refers to the accuracy of recommended items, usually expressed by precision; recall refers to the recommendation system's ability to recommend all correct recommendation lists, usually expressed by recall; diversity Sexuality refers to the size of the range of items recommended by the recommendation system, usually represented by diversity. In addition, for the evaluation of diversity, there are novelty, coverage, SERP indicators, etc. These indicators are helpful in evaluating the recommendation quality of the recommendation system, but they are not absolutely reliable. Therefore, it is recommended to refer to the business scenario and domain experts for a more detailed assessment.
(4) Data set division of recommendation system
An important task of the recommendation system is to extract valuable experience from a large number of users' operational behavior data and make personalized recommendations to new users. However, collecting, organizing, and analyzing massive data has become a huge and complex task. Therefore, the partitioning of data sets for recommendation systems is very important.
Usually, the training data set (Training Data Set) of the recommendation system consists of two parts: user data and product data. User data shows the user's preference for different products; product data provides detailed information, such as product name, description, price, category, illustrations, comments, pictures, etc. The training data set is generally arranged in chronological order and contains a certain amount of user and product data.
The test data set (Test Data Set) is the real data used by the recommendation system to evaluate the recommendation effect. It is much smaller than the training data set. The test data set generally includes user and item data, but only items that have not appeared in the training data set. In addition, the test data set may include validation or testing of the recommended results.
Development Data Set is also called "development set" or "validation set". The purpose of this data set is to predict algorithm performance and help select the best parameter combination. It contains part of the data of the training data set and does not participate in training, but can be used as a test set to evaluate the recommendation effect through different parameter settings. The development data set is invisible and does not affect the training process of the recommendation algorithm.
3. Recommendation system algorithm principles and application cases
(1) User-based collaborative filtering algorithm - user-based TOP-K recommendation algorithm
The user-based collaborative filtering algorithm is a main algorithm of the recommendation system and belongs to the model-based collaborative filtering algorithm. It plays a vital role in the recommendation system. The user-based collaborative filtering algorithm can automatically analyze user preferences and recommend relevant products to users.
The workflow of the user-based collaborative filtering algorithm is as follows:
Obtain the user's historical behavior data. This data shows what items the user has previously purchased and how related those items are to each other.
Categorize this behavioral data by user.
Based on the user's historical behavior, the similarity between each user and other users is calculated.
By comparing the similarities between different users, a list of recommended items is generated for each user.
Make personalized recommendations for users. This process is to provide users with more accurate recommendations by integrating the recommendation results of multiple users.
The user-based collaborative filtering algorithm has two recommendation strategies:
Item-based CF (item-based collaborative filtering algorithm). The algorithm makes recommendations based on the similarity between items. It first calculates the similarity matrix between the items that the user likes, and then uses this similarity matrix to generate a recommendation list for each user.
User-based CF (user-based collaborative filtering algorithm). The algorithm is user-centered and uses information such as interactive behavior and preference differences between users to make recommendations. It first calculates the common interest vector of items among users, and then uses this vector to generate a recommendation list for each user.
The evaluation indicators of user-based collaborative filtering algorithms are as follows:
Precision: refers to the accuracy of recommended items. If the items recommended by the recommendation system are truly interesting to the user, then it can be considered more accurate.
Recall: refers to the recommendation system's ability to recommend all correct recommendation lists. In the recommendation system, the higher the recall rate, the more products the user is actually interested in, and the greater the benefit of the system.
Coverage: refers to the size of the range of items recommended by the recommendation system. The higher the coverage rate, the more comprehensive the types of recommended items provided by the recommendation system, and the broader the coverage.
Novelty: refers to whether the products recommended by the recommendation system are the latest and eye-catching. The higher the novelty, the more users like the recommended products.
Diversity: refers to whether the products recommended by the recommendation system are even. The more diverse the products, the wider the user’s choice of recommended products.
(2) How to handle the recommendation of new products?
As a personalized recommendation engine, the recommendation system must consider the situation when new products appear in the recommendation system. Existing algorithms are mainly based on similarity algorithms to calculate the similarity between new products and existing products. Since new products may be intuitively recognized and familiar to users, their similarity is high, but their contribution to the final recommendation is very low. Therefore, how to effectively handle the recommendation of new products is very important.
For the recommendation of new products, the following methods can be used:
Recommended strategy optimization. Use machine learning methods to optimize recommendation strategies. For example, the reinforcement learning method is used to iteratively update the user's preferences for items to improve the accuracy and coverage of recommendations.
Reduce the similarity of new products. The misleading of the recommendation system can be reduced by reducing the similarity of new products. For example, a threshold can be set to determine the similarity of new products, and only products with a similarity higher than a certain threshold will be recommended.
Increase recommendation weight for new products. Adjusting the weight of new products can improve the effectiveness of the recommendation system by increasing their recommendation weight. For example, you can add the recommended position of new products to the recommended list, or increase the ranking weight of other recommended products.
(3) Application of recommendation system in e-commerce
The application of recommendation systems in the field of e-commerce is mainly divided into the following four parts:
Products Featured. The user-based collaborative filtering algorithm can provide product recommendations for users of e-commerce websites and promote interaction between users. E-commerce companies can also use recommendation systems to achieve personalized recommendations of products.
Merchant recommendations. Recommendation systems can also provide merchant recommendations for consumers on e-commerce websites to help consumers find merchants of interest. The accuracy of merchant recommendations depends on the merchant's own content and marketing strategies. The recommendation system can provide push messages about new stores.
Promotional recommendations. Recommendation systems can provide promotion recommendations to consumers on e-commerce websites. Promotional activities can help consumers seize the position of market competitors and attract more consumers to participate. Recommendation systems can provide suitable promotions to users by analyzing their preferences.
Personalized recommendations. Users of e-commerce websites often expect websites to recommend products to them based on their personal preferences, habits and spending power. Recommendation systems can provide consumers with personalized recommendations to meet their personalized needs of different types and needs.
There are still many problems to be solved in the application of recommendation systems in e-commerce. Among them, the efficiency of the recommendation algorithm is a difficult problem. The recommended algorithm needs to run in seconds and milliseconds to save resources while ensuring accuracy. At the same time, the accuracy, recall, coverage, novelty, sparsity and other indicators of the algorithm need to be high.