Recommender Systems TIETS43. Introduction. Kostas Stefanidis Fall

Transcription

1 + Recmmender Systems TIETS43 Intrductin Fall 2018 Kstas Stefanidis

2 selectin Amazn generates 35% f their sales thrugh recmmendatins recmmendatins

3 recmmendatins selectin

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5 recmmendatins selectin

6 : vide streaming, nline DVD, Blu-ray Disc rental : internet radi : image rganizer, image viewer.

7 Recmmender Systems Recmmender systems aim at suggesting t users items f ptential interest t them Tw main steps: Estimate a rating fr each item and user Recmmend t the user the item(s) with the highest rating(s) Why recmmendatins?

8 Why recmmendatins? Custmer/user Find interesting prducts/things t cnsume Narrw dwn the set f chices Suggest additinal things Help explring the space f ptins Discver new things Seller/prvider/generatr Persnalized service fr the user Increase trust Imprve custmer lyalty Increase sales Opprtunities fr prmtin, persuasin Obtain knwledge abut custmers

9 Purpse and success criteria Different perspectives/aspects Depends n dmain and purpse N hlistic evaluatin scenari exists Retrieval perspective Reduce search csts Prvide "crrect" prpsals Users knw in advance what they want Recmmendatin perspective Serendipity identify items frm the Lng Tail Users did nt knw abut existence

10 When des a RS d its jb well? Recmmend items frm the lng tail "Recmmend widely unknwn items that users might actually like!" 20% f items accumulate 74% f all psitive ratings

11 Purpse and success criteria Predictin perspective Predict t what degree users like an item Mst ppular evaluatin scenari in research Interactin perspective Give users a "gd feeling" Educate users abut the prduct dmain Cnvince/persuade users - explain Cnversin perspective Cmmercial situatins Increase "hit", "clickthrugh", "lkers t bkers" rates Optimize sales margins and prfit

12 The General Picture Recmmender systems fr estimating relevance prduct scre Recmmendatins Generatr X X X X X 2 0

13 The General Picture Cllabrative filtering: ask my friends abut the items they like friends data prduct scre Recmmendatins Generatr X X X X X 2 0

14 The General Picture items data descriptin price Cntent-based: shw me items similar t thse I previusly preferred prduct scre Recmmendatins Generatr X X X X X 2 0

15 The General Picture Persnalizatin user prfile prduct scre Recmmendatins Generatr X X X X X 2 0

16 The General Picture Cntextualizatin user cntext prduct scre Recmmendatins Generatr X X X X X 2 0

17 The General Picture Cmbine different mechanisms friends data prduct scre user prfile user cntext items data Recmmendatins Generatr X X X X descriptin price X 2 0

18 Tw main techniques: Cllabrative filtering Cntent-based recmmendatins

19 Cllabrative Filtering Wrd f muth! Use the wisdm f the crwd! Prduce interesting suggestins fr a user (filtering) by using the taste f ther users (cllabratin) X 4 X 1 T make suggestins/predict missing ratings, use: Similar users - user-based cllabrative filtering Similar items - item-based cllabrative filtering X 1 X 4 X 2 Assumptin: Users wh had similar tastes in the past, will have similar tastes in the future X 3

20 User-based Cllabrative Filtering Make suggestins based n preferences f similar users Given a user, identify his/her k mst similar users Csine similarity, Jaccard similarity Prduce recmmendatins based n the items that are liked by thse k users avg ratings, weighted schemes Expensive nline cmputatins

21 Item-based Cllabrative Filtering Explit relatinships between items Cmpute similarities between items Csine similarity, Jaccard similarity Keep fr each item nly the k mst similar items alng with their similarity scres Use similarities t calculate ratings fr items with n scres Other techniques cluster users and recmmend items the users in the cluster clsest t the active user like Back t this, in the cntext f grup recmmendatins

22 Cntent-based Recmmendatins Analyze data infrmatin abut items (dcs, music, etc.) Extract features fr items (actrs, genre, ect.) Recmmend items with features similar t items a user likes

23 Cld Start Prblem: An all-time classic prblem What happens with new users where we have n ratings yet? Recmmend ppular items Have sme start-up questins (e.g., prvide 10 restaurants yu lve ) What happens with new items? Cntent-based filtering techniques Pay a set f users/custmers t rate them (crwdsurcing)

24 Tpics: Cllabrative Filtering Cntent-based Filtering Knwledge-based Recmmendatins Hybrid Strategies Cntextual Recmmendatins Recmmendatins fr Grups Packages Recmmendatins Explanatins in Recmmender Systems Diversity in Recmmender Systems Fairness in Recmmender Systems Interactive Data Explratin

25 Structure Mdes f study Lectures, exercises, student presentatins in class Evaluatin Numeric 1-5 Curse Wrk and Assessment Assignments (3) (30%): Exercise prblems n the recmmendatin mdels studied, and shrt-answer questins n the papers and tpics discussed in class Participatin (10%): Participatin in the class Prject (60%) The prject will cnsist f the design and implementatin f an innvative prttype fr a recmmender system in a specific applicatin scenari, selected by the students (create grups f 2) The prject will be accmpanied with a shrt paper (7-8 pages lng), describing the prpsed ideas The prject will be evaluated at the end f the perid Extra pints will be given t students with an exceptinally gd prject

26 Prject Tpics Feel free t prpse yur wn tpic! Recmmendatins based n Linked (Open) Data Entity-based Recmmendatins in Knwledge Graphs Recmmendatins based n User Reviews Interactive Recmmendatin Crss-dmain Recmmendatins Natural Language Explanatins fr Recmmendatins Chart-like Explanatins fr Recmmendatins Diversity-aware Recmmendatins Fairness-aware Grup Recmmendatins

27 Prject Tpics Feel free t prpse yur wn tpic! Recmmending Persnalized News Recmmendatin f Representative Reviews in e-cmmerce Recmmending Prduct Packets t Custmers Insurance Recmmendatin Systems Recmmendatins in the Health Dmain Pints f Interest Recmmendatins Package Recmmendatins fr Trip Planning Activities Travel Rute Recmmendatins Presentatin f Recmmendatins fr Htels Curse Recmmendatins Query-based Music Recmmendatins

28 Questins?