LARS : A Location-Aware Recommender System

ICDE ‘12

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1. Introduction

• Traditional recommender systems– triple(user, rating, item)– (user id U) + (limit K)

• return K recommended items to U

• Locations– destinations check-in(Facebook, Foursquare)– user zip code(MovieLens)

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1.1 Motivation: A Study of Location-Based Ratings

• Preference locality

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1.1 Motivation: A Study of Location-Based Ratings

• Travel locality

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1.2 LARS - A Location-Aware Recommender

• (user, ulocation, rating, item)• (user, rating, item, ilocation)• (user,ulocation, rating, item, ilocation)

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1.2 LARS - A Location-Aware Recommender

• next……• 2. an overview of LARS• 3. spatial user ratings for non-spatial items• 4. non-spatial user ratings for spatial items• 5. spatial user ratings for spatial items• 6. experimental analysis

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2.1 LARS Query Model

• (user id U) + (limit K) + (location L)——> return K recommended items to U

• query ：– snapshot (one-time) queries– continuous queries

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2.2 Item-Based Collaborative Filtering

• Phase I: Model Building– 计算 item 间的相似度 sim

– 对于每个 item• 模型只会存储前 n 个相似度最高的 sim 值• n 为 user 个数

• Phase II: Recommendation Generation

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3 Spatial User Ratings For Non-spatial Items

• (user, ulocation, rating, item)• requirements

– Locality( 局部性 ) ：能对地点感知– Scalability( 可扩展性 ) ：能够对大量的用户进行

运算– Influence() ：用户能够改变感知的区域大小

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3.1 Data Structure

• partial pyramid structure— 局部锥形结构

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3.1 Data Structure

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3.2 Query Processing

• query processing steps– 1. 从最底层找起– 2. 如果没找到

• 去上一层找– 3. 直到找到为止

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3.2 Query Processing

• Continuous queries– ( 一边移动 & 一边查询 )– 1. 如果没有离开上一次查询时所在的 grid

• 还是原来熟悉的结果– 2. 否则

• 去上一层找，找到为止

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3.3 Data Structure Maintenance

• 当有 new users,ratings,items 时• Trigger: N% ( 才会启动 Maintenance)

• The maintenance will be amortized( 均摊 )

• Step I: Model Rebuild• Step II: Merging( 合并 )/Splitting( 分裂 )

Maintenance

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3.3.1 Cell Merging

• Impoves scalability– storage

• less CF models size( 只需储存于高层，底层不储存 )• 主要标准

– computational overhead• less maintenance computation 维护次数减少• less continuous query processing computation• 次要标准

• Hurts locality

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3.3.1 Cell Merging

• Two percentage values– locality_loss– scalability_gain

• A system parameter M∈ [0,1]• Merges if ：

– M 越小，则越倾向于合并

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3.3.1 Cell Merging

• Calculating locality_Loss– Sample– Compare

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3.3.1 Cell Merging• Calculating scalability_gain

– ( child cells ) / ( child cells + parent cell )– st

• 还是举之前的栗子– scalability_gain

• 4 child cells == 2GB• parent cell == 2GB• scalability_gain=50%

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3.3.1 Cell Merging• locality_loss=25%• scalability_gain=50%

• Assuming M=0.7• • but (0.3*50%)<(0.7*25%)• will not merge

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3.3.2 Cell Splitting

• 其效用与 Cell Merging 相反– Improves locality– Hurts scalability

• 计算与 Cell Merging 基本相同– locality_gain– scalability_loss

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Merging Splitting

locality_loss locality_gain

scalability_gain scalability_loss

4 Non-spatial User Ratings For Spatial Items

• (user, rating, item, ilocation)• travel locality• travel penalty

– – expensive computational overhead– so,employs “early termination”

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4.1 Query Processing

• Algorithm– 1. 找出全部 item 中， TravelPenalty 最小的 k 个 item ，将 k 个 item

按照 RecScore 从大到小排序，形成表 R– 2. 设 LowestRecScore 为 R 中最小的 ( 也就是第 K 个 ) RecScore 值– 3. 找出剩余 item 中 TravelPenalty 最小的 item

• 4. 设 MaxPossibleScore = MAX_RATING – TravelPenalty• 5.IF MaxPossibleScore <= LowestRecScore

– 6. 不再找了，直接 return R• 7. 算出此 item 的 RecScore• 8.IF RecScore > LowestRecScore

– RecScore 替换 LowestRecScore 进入 R– 重新找一个 LowestRecScore – 回到 3

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4.2 Incremental Travel Penalty Computation

• Incremental KNN– online– Exact– Expensive

• Penalty Grid– Offline– Less exact– Efficient

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5 Spatial User Ratings For Spatial Items

• (user, ulocation, rating, item, ilocation)• user partitioning & travel penalty

– can be used together– with very little change

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6 Experiment

• test recommendation quality– Foursquare ： real dataset– MovieLens ： real dataset

• test scalability and query efficiency– Synthetic ： synthetically generated dataset

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6 Experiment

• CF: item-based collaborative filtering• LARS-T: LARS with only travel penalty• LARS-U: LARS with only user partitioning• LARS: LARS with both techniques

• default parameter– M == 0.3– k == 10– the number of pyramid levels (h) == 8

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6.1 Recommendation Quality for Varying Pyramid Levels

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• 80% 训练， 20% 验证：• Measure （ Quality ）

– 统计预测的推荐结果 进入真实评分前 k( 默认 k=10) 的次数• 层次分太细，每个 grid 中 rating 太少

6.2 Recommendation Quality for Varying Values of k

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6.3 Storage Vs. Locality

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Note ： M 越小 ，越倾向于合并 M 越大，越倾向于分裂

6.4 Scalability

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Default ： M=0.3LARS is acceptable.

存储大小

平均维护时间

6.5 Query Processing Performance

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单次查询：LARS vs LARS-ULARS vs LARS-T通过对比可以发现之前两种技术所带来的时间上的优势

响应时间

平均响应时间

连续查询：CF 最快（那当然了 -_-# ）除此之外， LARS 最快