Sequential Recommendation System with Document Encoder
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Solution Overview
Problem
Known sequential recommendation systems fail to adequately utilize substantive information about document subjects in training data or live recommendations, leading to inaccurate predictions of next user actions.
Innovation Solution
A novel sequential recommendation system is developed that trains a machine learning model on document metadata and contents to generate more accurate predictions of next user actions, incorporating a document encoder, next document prediction module, and cold start document prediction module to improve recommendation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If known sequential recommendation systems are used, then the system structure is simple, but the prediction accuracy of next user actions is insufficient
Solution Approach 1:
The system segments the recommendation task into multiple specialized modules: a document encoder module that processes document metadata and content, a next document prediction module that generates predictions, and a cold start document prediction module that handles new documents. This segmentation allows each module to specialize in specific aspects of the prediction task, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The system transitions from traditional sequential recommendation that only considers user action sequences to a multi-dimensional approach that incorporates document metadata dimensions (titles, descriptions, categories) and content dimensions. This additional dimensional information enriches the feature space for predictions, significantly improving prediction accuracy beyond what simple sequential models can achieve.
2Measurement precision
If document metadata and contents are incorporated into training, then prediction accuracy improves, but information processing complexity increases
Solution Approach 1:
The document encoder module extracts key features from document metadata and content, separating the information extraction process from the prediction process. This extraction mechanism identifies and isolates the most relevant features from large amounts of document information, reducing the complexity of information processing while maintaining high prediction accuracy by feeding only the essential extracted features to the prediction module.
Solution Approach 2:
The document encoder serves as an intermediary between the raw document metadata/content and the next document prediction module. This intermediary process transforms unstructured or semi-structured document information into structured numerical representations (embeddings), mediating the complexity by providing a standardized interface that simplifies the downstream prediction task while充分利用 the rich document information.
3Adaptability or versatility
If cold start documents are handled separately, then recommendation coverage improves, but system complexity increases
Solution Approach 1:
The cold start document prediction module performs preliminary actions by handling new, unseen documents before they would normally fail in the standard prediction pipeline. This module is pre-configured to handle documents without historical interaction data, performing the necessary encoding and prediction steps in advance, thereby improving recommendation coverage for cold start scenarios without requiring complex runtime decision logic.
Solution Approach 2:
The system achieves universality by designing a unified architecture where the same document encoder and prediction modules handle both standard documents with interaction history and cold start documents without history. The cold start document prediction module provides multi-functionality by serving as both a fallback mechanism for new documents and an integral part of the overall recommendation system, thereby improving coverage without proportionally increasing complexity through specialized separate systems.
Data Source
AI summary
A method for predicting a next user selection in an electronic user interface includes receiving, from a user, a sequence of selections of documents and generating, for each document in the sequence, a respective attribute vector. The attribute vector includes a numerical attribute vector portion representative of numerical attributes of the document, a category attribute vector portion representative of category information of the document, a text content vector portion representative of text content of the document, and an image content vector portion representative of an image in the document. The method further includes inputting the attribute vectors of the sequence into a machine learning model, and outputting, to the user, in response to the sequence of selections, a predicted next document selection according to an output of the machine learning model.