Location-Based Recommendation System Personalization
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Solution Overview
Problem
Current systems lack an effective method to determine and recommend related places based on content items and user activity information, failing to provide personalized and contextually relevant location-based recommendations.
Innovation Solution
A Location-Based Recommendation System (LBRS) that utilizes a place identifier, user activity tracker, and recommendation engine to process location and content information, generating concept vectors for places and user preferences, enabling the identification and ordering of similar places based on distance, user interests, and activity data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional place recommendation systems are used, then place recommendations can be provided, but the recommendations lack personalization and contextual relevance
Solution Approach 1:
The system performs preliminary actions by tracking user activities, generating concept vectors for users based on their behavior patterns, and pre-computing place characteristics before recommendations are needed. This enables personalized recommendations without losing valuable user activity information, as the system proactively processes and stores user preferences and behaviors for later retrieval and matching with relevant places.
2Measurement precision
If multiple factors are considered for place recommendation, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The system transforms complex multi-factor recommendation requirements into manageable parameters by converting user preferences, place characteristics, and user-place relationships into numerical concept vectors. This parameter transformation enables accurate recommendations through mathematical operations on vectors while maintaining system tractability, avoiding the need for complex rule-based systems.
Solution Approach 2:
The patent replaces traditional mechanical rule-based recommendation systems with a mathematical vector-space model. Instead of using complex if-then rules to evaluate multiple factors, the system uses vector similarity computations to determine recommendation accuracy, substituting mechanical decision logic with mathematical operations that are both precise and computationally efficient.
Data Source
AI summary
Techniques for providing location-based recommendations are described. Some embodiments provide a Location-Based Recommendation System (“LRBS”) that provides recommendations regarding physical places based on content items, such as Web pages, user reviews, directory listings, or the like, that describe or otherwise reference those places. In one embodiment, the LBRS is configured to, in response to an indication of a first place, determine one or more other places that are similar to the first place, and then provide indications of the determined places as recommendations to a user or other entity. In another embodiment, the LBRS is configured to, in response to an indication of a user, determine one or more places that may be of interest to the user, and then provide indications of the determined places as recommendations. In some embodiments, the LBRS may determine recommendations based on content vectors associated with places and/or users.