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Automated Discovery of Recommendation Knowledge David McSherry

Automated Discovery of Recommendation Knowledge David McSherry School of Computing and Information Engineering University of Ulster. +. Overview. Approaches to retrieval in recommender systems Ru le- b ased r etr i eval (of c ases) in Rubric

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Automated Discovery of Recommendation Knowledge David McSherry

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  1. Automated Discovery of Recommendation Knowledge David McSherry School of Computing and Information Engineering University of Ulster +

  2. Overview • Approaches to retrieval in recommender systems • Rule-based retrieval (of cases) in Rubric • Automating the discovery of recommendation rules • Role of default preferences in rule discovery • Related work • Conclusions +

  3. The Recommendation Challenge • Often we expect salespersons to make reliable recommendations based on limited information: • I’m looking for a 3-bedroom detached property • To recommend an item with confidence, a salesperson has to consider: • The customer’s known preferences • The available alternatives • All features of the recommended item  including features notmentioned by the customer

  4. Are Recommender Systems Reliable? • Features not mentioned in the user’s query are typically ignored in: • Nearest neighbour (NN) retrieval • Decision tree approaches • Multi-criterion decision making • Assumed(or default) preferences are sometimes used for attributes like price • But for many attributes, no assumptions can be made about the user’s preferences

  5. Preferences Pyramid Known preferences Default preferences beds = 3 type = detached Unknown preferences ..., reasonably priced, ..., ..., location = A, ...,

  6. CBR Recommender Systems • Descriptions of available products (e.g. houses) are stored as cases in a product dataset e.g. Loc Beds Type Weight: (3) (2) (1) Case 1: A 3 semi Case 2: B 4 det Case 3: B 3 det and retrieved in response to user queries

  7. Inductive Retrieval • Not only are the user’s unknown preferences ignored - the user is prevented from expressing them 4 Bedrooms? Case 2 (B, 4, det) 3 det Case 3 (B, 3, det) Type? semi Case 1 (A, 3, semi)

  8. Inductive Retrieval • The recommended case exactly matches the user’s known preferences - but what if she prefers location A? 4 Bedrooms? Case 2 (B, 4, det) 3 det Case 3 (B, 3, det) Type? semi Case 1 (A, 3, semi)

  9. The standard CBR approach is to recommend the most similar case The similarity of a case C to a query Q over a subset AQ of the product attributes A is: where wa is the weight assigned to a Nearest Neighbour Retrieval

  10. Incomplete Queries in NN Loc Beds Type (3) (2) (1) Q : 3 det Sim Case 1: A 3 semi 2 Case 2: B 4 det 1 Case 3: B 3 det 3 most-similar(Q) = {Case 3}

  11. Incomplete Queries in NN Loc Beds Type (3) (2) (1) Q : 3 det Sim Case 1: A 3 semi 2 Case 2: B 4 det 1 Case 3: B 3 det 3 most-similar(Q) = {Case 3} • Again, Case 3 is a good recommendation if the user happens to prefer location B

  12. Incomplete Queries in NN Loc Beds Type (3) (2) (1) Q* : A 3 detSim Case 1: A 3 semi 5 Case 2: B 4 det 1 Case 3: B 3 det 3 most-similar(Q*) = {Case 1} • But not if she prefers location A

  13. Rule-Based Retrieval in Rubric • In rule-based retrieval, a possible recommendation rule for Case 3 might be: Rule 1: if beds = 3 and type = det then Case 3 • Given a target query, a product dataset, and a set of recommendationrules, Rubric: • Retrieves the case recommended by the first rule that covers the target query • If none of the available rules covers the target query, it abstains from making a recommendation

  14. Dominance Rules • For any case C and query Q, we say that Q → C is a dominance rule if: most-similar(Q*) = {C} for all extensionsQ* of Q • As Rule 1 is not a dominance rule for Case 3, it is potentially unreliable: Rule 1: if beds = 3 and type = det then Case 3

  15. A Dominance Rule for Case 3 Loc Beds Type (3) (2) (1) Q : B3Sim Case 1: A 3 semi 2 Case 2: B 4 det 3 Case 3: B 3 det 5 most-similar(Q) = {Case 3}

  16. A Dominance Rule for Case 3 Loc Beds Type (3) (2) (1) Q : B3SimMax Case 1: A 3 semi 2 3 Case 2: B 4 det 3 4 Case 3: B 3 det 5 • As Cases 1 and 2 can never equal the similarity of Case 3, a dominance rule for Case 3 is: Rule 2: if loc = B and beds = 3 then Case 3

  17. Coverage of a Dominance Rule • A dominance rule Q→ C can be applied to any query Q* such that QQ* since by definition: most-similar(Q*) = {C} • Also by definition, most-similar(Q**) = {C} for any extension Q** of Q* • So no other case can equal the similarity of C regardless of the user’s unknown preferences

  18. The Role of Case Dominance • A given case C1dominates another case C2 with respect to a query Q if: Sim(C1, Q*) > Sim(C2, Q*) for all extensions Q* of Q (McSherry, IJCAI-03) • So Q→ C is a dominance rule if and only if C dominates all other cases with respect to Q • This is not the same as Pareto dominance

  19. Identifying Dominated Cases • A given case C1 dominates another case C2 with respect to a query Q if and only if: (McSherry, IJCAI-03) • Cases dominated by a given case can thus be identified with modest computational effort

  20. B, 3, det B, 3 B, det 3, det B 3 det nil Dominance Rule Discovery(McSherry & Stretch, IJCAI-05) • Our algorithm targets maximally general dominance rules Q→ Csuch that Q description(C) Description of Case 3 Case 3 dominates Case 1 and Case 2 with respect to this query

  21. Complexity of Rule Discovery • Our discovery algorithm is applied with each case in turn as the target case • For a product dataset with n cases and k attributes, where n  2k, the worst-case complexity is: O(k  n2  2k) • If n < 2k, the worst-case complexity is: O(k  n  22k)

  22. Maximum Rule-Set Size In a dataset with k attributes, the number of rules discovered for a target case can never be more than kCk/2(McSherry & Stretch, IJCAI-05) With 1,000 products and 9 attributes, the maximum number of discovered rules is 126,000 Rule-set sizes tend to be much smaller in practice

  23. Digital Camera Case Base Source: McCarthy et al. (IUI-2005) No of cases: 210 Attributes: make (9), price (8), style (7), resolution (6), optical zoom (5), digital zoom (1), weight (4), storage type (2), memory (3) Discovered Rule: if make = toshiba and style = ultra compact and optical zoom = 3 then Case 201

  24. Discovered Rule-Set Sizes Digital Camera Case Base (k = 9)

  25. Lengths of Discovered Rules Digital Camera Case Base (k = 9)

  26. Limitations of Discovered Rules Example Rule if make = sony and price = 336and style = compact and resolution = 5 and weight = 236 then Case 29 Problem Exact numeric values (e.g., price, weight) make the rule seem unnatural/unrealistic They also limit its coverage Solution Assume the preferred price and weight are the same for all users

  27. LIB and MIB Attributes • A less-is-better (LIB) attribute is one that most users would prefer to minimise e.g. price, weight • A more-is-better (MIB) attribute is one that most users would prefer to maximise e.g. resolution, optical zoom, digital zoom, memory • Often in NN retrieval, LIB and MIB attributes are treated as nearer-is-better attributes: • How much would you like to pay? 300

  28. LIB and MIB Attributes • A less-is-better (LIB) attribute is one that most users would prefer to minimise e.g. price, weight • A more-is-better (MIB) attribute is one that most users would prefer to maximise e.g. resolution, optical zoom, digital zoom, memory • Often in NN retrieval, LIB and MIB attributes are treated as nearer-is-better attributes: • How much would you like to pay? 300 • This doesn’t make sense, as it implies that the user would prefer to pay 310 than 280

  29. Role of Default Preferences in Rule Discovery(McSherry & Stretch, AI-2005) • We assume the preferred value of a LIB/MIB attribute is the lowest/highest value in the case base • These preferences are represented in a default query: QD : price = 106, memory = 64, resolution = 14, optical zoom = 10, digital zoom = 8, weight = 100 • In the dominance rules Q → C now targeted by our algorithm, Q includes the default preferences in QD • Thus the assumed preferences are implicit in the discovered rules

  30. Similarity to the Default Query • We use the standard measure for numeric attributes: where x is the value in a givencase and y is the preferred value • For a LIB attribute:

  31. Digital Camera Case Base No of cases: 210 Attributes:make, price, style, resolution, optical zoom, digital zoom, weight, storage type, memory LIB attributes: price, weight MIB attributes: resolution, optical , digital, memory Discovered Rule: if make = sony and style = compact then Case 29

  32. Reduced Complexity of Rule Discovery(e.g., from 512 candidate queries to 8) QD {sony, compact, memory stick} QD {sony, compact}QD {sony, memory stick} QD  {compact, memory stick} QD {sony} QD {compact} QD  {memory stick} QD Dominance Rule Discovery for Case 29

  33. Reduced Length of Discovered Rules DPs = Default Preferences

  34. Recommendability of Cases • Only 56 of the 210 cases can be the most similar case for any query that includes the default query QD • The reason is that most cases are dominated with respect to the default query • For most of the 56 non-dominated cases, only a single dominance rule was discovered • The discovered rules cover 29% of all queries over the attributes make, style, and storage type

  35. Retrieving Stories for Case-Based Teaching(Burke & Kass, 1996) • Rule-based retrieval of stories or lessons learned by experienced salespersons • Retrievalis conservative, opportunistic,and non-mandatory • A story is retrieved at the system’s initiative and only if highly relevant • By design, retrieval in Rubric is also conservative and non-mandatory (and potentially opportunistic) • Easily combined with NN retrieval of a less strongly recommended case if no rule covers a given query

  36. Incremental Nearest Neighbour (iNN)(McSherry, IJCAI-03, AICS-05, AIR 2005) • A conversational CBR approach in which: • Question selection is goal driven(i.e., maximise number of cases dominated by a target case) • Dialogue continues until it can be safely terminated(i.e., no other case can exceed the similarity of the target case) • Relevance of any question can be explained(e.g., ability to confirm the target case) • Recommendations can be justified(i.e., unknown preferences cannot affect the outcome)

  37. Demand Driven Discovery of Recommendation Knowledge in Top Case Top Case:What is the preferred make? User: sony Top Case: The target case is: Case 40: sony, 455, ultra compact, 5, 4, 4, 298, MS, 32 What is the preferred style? User:why Top Case:Because if style = ultra compact this will confirm Case 40 as the recommended case What is the preferred style? User: compact Top Case: The recommended case is: Case 29: sony, 336, compact, 5, 3, 4, 236, MS, 32

  38. Conclusions • Benefits of retrieval based on dominance rules: • Provably reliable because account is taken of the user’s unknown preferences • Benefits of default preferences: • An often dramatic reduction in average length of the discovered rules • Increased coverage of queries representing the user’s personal preferences • Reduced complexity of rule discovery

  39. References Burke, R. and Kass, A. (1996) Retrieving Stories for Case-Based Teaching. In Leake, D. (ed.) Case-Based Reasoning: Experiences, Lessons & Future Directions. Cambridge, MA: AAAI Press, 93-109 McCarthy, K., Reilly, J., McGinty, L. and Smyth, B. (2005) Experiments in Dynamic Critiquing. Proceedings of the International Conference on Intelligent User Interfaces, 175-182 McSherry, D. (2003) Increasing Dialogue Efficiency in Case-Based Reasoning without Loss of Solution Quality. Proceedings of the 18th International Joint Conference on Artificial Intelligence, 121-126 McSherry, D. (2005) Explanation in Recommender Systems. Artificial Intelligence Review24 (2) 179-197 McSherry, D. (2005) Incremental Nearest Neighbour with Default Preferences. Proceedings of the 16th Irish Conference on Artificial Intelligence and Cognitive Science, 9-18 McSherry, D. and Stretch, C. (2005) Automating the Discovery of Recommendation Knowledge. Proceedings of the 19th International Joint Conference on Artificial Intelligence, 9-14 McSherry, D. and Stretch, C. (2005) Recommendation Knowledge Discovery. Proceedings of the 25th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence

  40. Acknowledgements • Thanks to: • Eugene Freuder, Barry O’Sullivan, Derek Bridge, Eleanor O’Hanlon (4C) • Chris Stretch (co-author, IJCAI-05 and AI-2005) • Kevin McCarthy, Lorraine McGinty, James Reilly, Barry Smyth (UCD) for the digital camera case base

  41. Compromise-Driven Retrieval(McSherry, ICCBR-03, UKCBR-05) • Similarity and compromise (unsatisfied constraints) play complementary roles • Queries can include upper/lower limits for LIB/MIB attributes (used only in assessment of compromise) • Every case in the product data set is covered by one of the recommended cases • That is, one of the recommended cases is at least as similar and involves the same or fewer compromises

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