Content Recommendation Engine Search Session Grouping
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Solution Overview
Problem
Users face challenges in efficiently searching for content due to the vast number of options available, leading to frustration and time consumption in finding new content.
Innovation Solution
A method of processing searches by determining the time between consecutive searches, grouping them into search sessions and sub-sessions, and determining the intended search of each sub-session to improve content recommendation systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually search for content using search functions, then they can find specific content, but it becomes frustrating and time consuming due to the vast number of options
Solution Approach 1:
The system analyzes user search behavior by monitoring consecutive searches, determining time intervals between them, and grouping them into sessions. This feedback mechanism allows the system to understand user intent more accurately and provide better content recommendations, reducing the time users spend searching.
Solution Approach 2:
The system performs preliminary analysis of search patterns by grouping consecutive searches into sessions and determining intended searches before users complete their manual searching. This preliminary understanding of user intent allows the system to proactively provide relevant content recommendations.
2Adaptability or versatility
If the content library is expanded to provide more variety, then content availability increases, but users become overwhelmed and continue viewing familiar content rather than trying new options
Solution Approach 1:
The system segments the vast content library into manageable groups based on user search behavior patterns. By analyzing consecutive searches and grouping them into sessions, the system identifies user intent and presents segmented, relevant content recommendations rather than overwhelming users with the entire content library.
Solution Approach 2:
The system applies local quality by providing customized content recommendations tailored to each user's specific search intent and behavior patterns. Instead of treating all users equally, the system adapts the content presentation to match individual user needs and preferences.
3Device complexity
If the system processes each search individually, then search processing is simple, but it fails to understand the user's intended search due to spelling errors or lack of knowledge
Solution Approach 1:
The system merges multiple consecutive searches into unified search sessions, combining individual search queries to determine the user's intended search. By analyzing patterns across multiple searches within a session, the system overcomes individual search limitations such as spelling errors or incomplete queries.
Data Source
AI summary
There is provided method of processing searches input into a content recommendation engine (CRE). The CRE is adapted to receive searches and provide one or more content recommendations based on the received searches for a user of a content distribution system having a plurality of users. The method comprises determining a time between consecutive searches of a plurality of searches; grouping one or more of the plurality of searches into a search session based on the determined time; grouping searches in the search session into a sub-session; and determining an intended search of the sub-session. In another embodiment, the method comprises determining a similarity of each search of a plurality of searches; and mapping each search of the plurality of searches to an intended search based on the determined similarity.