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Auralist: Introducing Serendipity ( 惊喜度 ) into Music Recommendation WSDM ’12 (ACM international conference on Web Search and Data Mining)

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Presentation on theme: "Auralist: Introducing Serendipity ( 惊喜度 ) into Music Recommendation WSDM ’12 (ACM international conference on Web Search and Data Mining)"— Presentation transcript:

1 Auralist: Introducing Serendipity ( 惊喜度 ) into Music Recommendation WSDM ’12 (ACM international conference on Web Search and Data Mining)

2 1. INTRODUCTION The majority of research focuses on improving the accuracy of recommendation The dangers – 1. produces boring and ineffective recommendations – 2. harms a user's personal growth and experience

3 1. INTRODUCTION Contributions : 1. Balance the conflicting goals – accuracy – diversity – novelty – serendipity 2. Use metrics to measure all three non-accuracy factors simultaneously

4 1. INTRODUCTION Next… 2. Why accuracy is not enough(and the other three properties) 3. The auralist framework(and algorithm) 4. Evaluation(Quantitative evaluation) 5. User study(Quantitative evaluation)

5 2. WHY ACCURACY IS NOT ENOUGH Firstly, the boring symbols 用户集 评分矩阵 user u 的 Top-N 推荐 物品集 user u 的验证集 user u 的训练集 item i 的热度 LDA topic 的集合 LDA 物品 - 话题 矩阵 歌手 ( 物品 ) i 的 听众数目

6 2.1 ACCURACY average Top-N Recall 直观上:推荐列表的物品,在验证集中的数目(越小越好)

7 2.1 ACCURACY average Rank score 其中 直观上:所推荐的物品,在验证集中的受喜爱程度 喜爱程度通过排名表示,所以越小越好

8 2.1 ACCURACY Produce recommendations that appear supercially “good” But are in fact inferior in terms of actual user satisfaction

9 2. DIVERSITY( 多样性 ) 直观上:位于推荐列表中所有物品,两两的余弦相似度(越小越好)

10 2. NOVELTY( 新颖性 ) 直观上: higher values mean that more globally “unexplored" items are being recommended

11 2. SERENDIPITY( 惊喜度 ) 直观上:训练集的物品与推荐结果的物品 两两之间的相似度 (越小越好)

12 3. THE AURALIST FRAMEWORK Basic – LDA model Hybrid – Listener Diversity + Basic ---> Community-Aware – Declustering + Basic ---> Bubble-Aware Full

13 3.1 BASIC AURALIST Using LDA ( Latent Dirichlet Allocation ) 即:将原来文档中,向量空间的词的维度 转变为 ”Topic” 的维度

14 3.1 BASIC AURALIST 举个栗子 一个文档 A ,包含 “ 电脑 ” 和 “ 微机 ” 这两个词。 将文档 A 向量化后可能是, “ 电脑 ” 这个词是全部词 汇中的第 2 维,而 “ 微机 ” 是第 3 维。 维上的投影简单看作是其 TF( 文档中出现的次数 ) 。 A={x,1,1,x,...,x}

15 3.1 BASIC AURALIST 词的向量空间 A={x,1,1,x,...,x} 在向量空间中, “ 电脑 ” 及 “ 微机 ” 这两个维度被认为 正交,即两个词表示了完全不同的意义。 将两个词的维度 “ 捏合 ” 为一个 Topic 的维度,词在 Topic 中表示为权重。 Topic 的向量空间 A={y,(p1+p2),y,...,y} 降低了维度 ( 好像很好的样子 )

16 3.1 BASIC AURALIST Document : TheWilliam Randolph Hearst Foundation will give $1.25 million to Lincoln Center, Metropolitan Opera Co., New York Philharmonic and Juilliard School. “Our board felt that we had a real opportunity to make a mark on the future of the performing arts with these grants an act every bit as important as our traditional areas of support in health, medical research, education and the social services,” Hearst Foundation President Randolph A. Hearst said Monday in announcing the grants. Lincoln Center’s share will be $200,000 for its new building, which will house young artists and provide new public facilities. The Metropolitan Opera Co. and New York Philharmonic will receive $400,000 each. The Juilliard School, where music and the performing arts are taught, will get $250,000. The Hearst Foundation, a leading supporter of the Lincoln Center Consolidated Corporate Fund, will make its usual annual $100,000 donation, too.

17 3.1 BASIC AURALIST Words ---> Topics

18 3.1 BASIC AURALIST

19 word user topic user- community document artist Artist-based LDA model

20 3.1 BASIC AURALIST similarity between artist topic vectors the score that user u associates to item I – The LDA similarity used directly for item-based recommendation 对所有 item 的 Basic 值排序,得到推荐列表

21 3.2 Two hybrid versions of Auralist “A” that includes – Artist-based LDA – Listener Diversity – Declustering

22 3.2.1 Community-Aware Auralist Listener Diversity(the entropy over its topic distribution) The Rank Give it some offset The offset

23 3.2.2 Bubble-Aware Auralist The rank

24 3.2.2 Bubble-Aware Auralist algorithm

25 4. EVALUATION 1. Basic Auralist 2. the state-of-the art : – Implicit SVD( 奇异值分解 ) method 3. Community-aware Auralist (λ1=0.05) 4. Bubble-aware Auralist (λ2=0.2) 5. Full Auralist

26 4.1 DATASET user.getTopArtists() from the Last.fm API Quantity : 360k users

27 4.2 Basic Auralist Recommendation

28 4.2 Hybrid versions of Auralist Accuracy performance

29 4.2 Hybrid versions of Auralist Diversity, Novelty, Serendipity performance

30 5. USER STUDY Full Auralist λ1=0.03 λ2=0.20

31 5.1 Experimental Method involved 21 participants included a mix of – under/post graduates – men/women – between the ages of18-27 – varying nationalities

32 5.2 User Ratings

33

34 5.2 User Satisfaction “[Full Auralist] was more satisfying because it bintroduced me to new artists. [Basic] was lled entirely with new artists which, while very good, were things that I listened to all the time on a regular basis. [Full Auralist] had artists that were of the same quality of those I listen to but which I'd never heard of.“ “I found [the Full Auralist list] more surpris- ing than [Basic]. Most artists I had not heard of (which is what I prefer). Listening to them gave me at least ve new artists I could look into and use in the future.“ “While I enjoyed the songs on the [Full Au- ralist] list less, I liked that there was more new music on it than the rst list. So I'm going to say that I preferred the [Full Auralist] list.“ “[The Basic list was better], more familiar music & more my taste, although [Full Auralist] introduced me to a few good bands.“ “[The Full Auralist list] was way too jazzy, and had very few artists I connected with imme- diately. While [the Basic list] had a vast majority of artists I knew well and have opinions of, the few unknowns were really very congenial." 以下为测试者的言论,对 Full Auralist 各种赞扬


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