The recommendation system is a powerful tool in the era of big data. It can improve user experience, increase user stickiness, promote sales conversion, and improve marketing efficiency for enterprises. However, it is not easy to build a successful recommendation system. It needs to comprehensively consider various factors and continuously iterate and optimize according to business scenarios, user needs, and data changes.
This article will take a B2B2C company, one of the world's top 500 companies, as a case object, and discuss in depth the technical solutions and methods it adopts in building and evolving its big data recommendation system at different stages. This article will lead you through the various stages of the recommendation system step by step from scratch, thus completing the entire life cycle of the recommendation system from inception to maturity.
▊ Phase 1: Proof of concept, rapid realization of minimum viable recommendation
At this stage, we added a recommendation column on the website to provide the company with a simple and effective recommendation solution, and observed its impact on the core KPI (sales increase). We use third-party libraries such as Python and SKlearn to implement recommendation logic based on collaborative filtering and association algorithms, and evaluate the recommendation effect through AB testing.
When doing business understanding and project planning at the very beginning, we will introduce in detail the steps of planning, design, code deployment, testing, and verification of traffic data embedding points, and use Python and other libraries for data cleaning, analysis, and visualization; more What is important is how to define business goals and coordinate with the sub-goals of the recommender system.
We use Google Analytics 360 for data embedding and analysis, and use Python's Pandas, Numpy, Matplotlib, etc. for data processing and visualization, such as using SVD, GBDT and other algorithms in SKlearn to implement collaborative filtering recommendations, and using MLlib in FPGrowth and The Prefixspan algorithm implements the method recommended by association rules.
▊ Phase 2: Basic construction, from 0 to building a complete and scalable recommendation architecture
At this stage, we build a complete and scalable recommendation system architecture, covering two scenarios of community content and product recommendation, and use multiple indicators to measure the recommendation effect.
We use technologies such as PySpark and HiveSQL to complete data synchronization, cleaning, calculation and other processes, and use modes such as Learn2Rank for sorting optimization. We use AWS EMR, Redis, Java and other technologies to build distributed computing and API service clusters, and use NLP technology for content analysis and label extraction. The core technologies include:
Use technologies such as PySpark and HiveSQL to complete data synchronization, cleaning, calculation and other processes.
Use ALS, FM and other algorithms in PySpark to realize model-based collaborative filtering recommendation.
Use Redis as the cache database to cache recommendation results.
Use algorithms such as XGBoost to realize sorting optimization in Learn2Rank mode.
Use Jieba word segmentation, TF-IDF, Word2Vec and other technologies for content analysis and tag extraction.
Throughout the process, we provide customers with an end-to-end recommendation service, that is, customers only need to call our recommendation API interface on the website to realize the recommendation application service, and all other processes are implemented by us.
▊ Phase 3: Recommendation enhancement, integration and linkage of online and offline recommended scenarios
At this stage, we added multiple recommendation scenarios such as search recommendation, online event recommendation, and offline customer sales support, and realized the linkage of online and offline data and applications. We adjust the recommendation strategy according to different scenarios, goals, and objects, and consider regional preferences, industry characteristics, and cross-regional sales policies.
We use ES as a search engine, and combine PageRank, social network detection and other technologies to realize the mining and processing of multivariate data relationships. In this process, ES mainly undertakes the calculation of text similarity, and its role is text storage and text similarity recall; at the same time, the similarity score is used as one of the weights of fine sorting. In terms of model algorithms, we have added more methods based on social relations, text embedding, and multi-objective-based regression and classification prediction to meet the needs of different business scenarios.
We use CRM data, sales data, marketing activity data, etc. to enrich user portraits and behavior patterns, and adjust the strategy of recommendation and fine sorting according to factors such as regional preferences, industry characteristics, and cross-regional sales policies; at the same time, use CRM data to build user portraits , and combine user behavior data to mine more user behavior patterns.
▊ Phase 4: Real-time calculation, recommended upgrade and real-time transformation of the whole process
At this stage, we improve the real-time performance of the recommendation system, so that new registered users, newly generated content, and the latest user behavior can be fed back to the recommendation results in a timely manner. We have increased the monitoring of evaluation indicators such as real-time, diversity, and freshness.
We use message queues, API, PMML, etc. to realize data and service exchange between offline and online environments, and use stream processing frameworks and storage technologies such as Spark Structured Streaming, Delta Lake, etc. to complete online computing and real-time data storage. Real-time applications of full features, full data, full feedback, and full models are supported through processes such as real-time data processing, feature extraction and offline feature combination, recommendation prediction, real-time fine sorting, and re-ranking (such as popularity reduction).
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