automated discovery of recommendation knowledge david mcsherry

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Automated Discovery of Recommendation Knowledge

David McSherry

School of Computing and Information EngineeringUniversity of Ulster

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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

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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 not mentioned by the customer

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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

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..., reasonably priced, ...,

Preferences Pyramid

Knownpreferences

beds = 3type = detached

..., location = A, ...,

Unknownpreferences

Defaultpreferences

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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

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Inductive Retrieval

Bedrooms? Case 2 (B, 4, det)4

Type?

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Case 3 (B, 3, det)det

Case 1 (A, 3, semi)semi

Not only are the user’s unknown preferences ignored - the user is prevented from expressing them

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Inductive Retrieval

Bedrooms? Case 2 (B, 4, det)4

Type?

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Case 3 (B, 3, det)det

Case 1 (A, 3, semi)semi

The recommended case exactly matches the user’s known preferences - but what if she prefers location A?

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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

QAa

aa QCsimwQCSim ),(),(

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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}

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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

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Incomplete Queries in NN

Loc Beds Type

(3) (2) (1) Q* : A 3 det Sim

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

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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 recommendation rules, 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

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For any case C and query Q, we say that Q → C is a dominance rule if:

most-similar(Q*) = {C}

for all extensions Q* 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

Dominance Rules

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A Dominance Rule for Case 3

Loc Beds Type

(3) (2) (1)

Q : B 3 Sim

Case 1: A 3 semi 2

Case 2: B 4 det 3

Case 3: B 3 det 5

most-similar(Q) = {Case 3}

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A Dominance Rule for Case 3

Loc Beds Type

(3) (2) (1)

Q : B 3 Sim Max

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

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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

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The Role of Case Dominance

A given case C1 dominates 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

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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

QAAa

aa CCsimwQCSimQCSim )),(1(),(),( 2121

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Dominance Rule Discovery(McSherry & Stretch, IJCAI-05)

Our algorithm targets maximally general dominance rules Q → C such that Q description(C)

B, 3, det

B 3 det

B, 3 B, det 3, det

nil

Case 3 dominates Case 1 and Case 2 with respect to this query

Description of Case 3

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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)

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In a dataset with k attributes, the number of rules discovered for a target case can never be more than

kCk/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

Maximum Rule-Set Size

No. of Attributes: 4 5 6 7 8 9 10

Maximum: 6 10 20 35 70 126 252

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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

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Discovered Rule-Set Sizes

Digital Camera Case Base (k = 9)

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Lengths of Discovered Rules

Digital Camera Case Base (k = 9)

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Limitations of Discovered Rules

Example Rule

if make = sony and price = 336 and 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

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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

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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

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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

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Similarity to the Default Query

We use the standard measure for numeric attributes:

where x is the value in a given case and y is the preferred value

For a LIB attribute:

minmax1),(

yx

yxsim

minmax

maxmin),(

x

xsim

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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

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QD {sony, compact, memory stick}

QD {sony, compact} QD {sony, memory stick} QD {compact, memory stick}

QD {sony} QD {compact} QD {memory stick}

QD

Reduced Complexity of Rule Discovery(e.g., from 512 candidate queries to 8)

Dominance Rule Discovery for Case 29

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Reduced Length of Discovered Rules

DPs = Default Preferences

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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

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Retrieving Stories for Case-Based Teaching(Burke & Kass, 1996)

Rule-based retrieval of stories or lessons learned by experienced salespersons

Retrieval is 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

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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)

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Demand Driven Discovery of Recommendation Knowledge in Top Case

Top Case: What is the preferred make? User: sonyTop Case: The target case is:

Case 40: sony, 455, ultra compact, 5, 4, 4, 298, MS, 32What is the preferred style?

User: whyTop Case: Because if style = ultra compact this will confirm Case 40 as the recommended case

What is the preferred style? User: compactTop Case: The recommended case is:

Case 29: sony, 336, compact, 5, 3, 4, 236, MS, 32

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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

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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 Review 24 (2) 179-197McSherry, D. (2005) Incremental Nearest Neighbour with Default Preferences. Proceedings of the 16th Irish

Conference on Artificial Intelligence and Cognitive Science, 9-18McSherry, D. and Stretch, C. (2005) Automating the Discovery of Recommendation Knowledge.

Proceedings of the 19th International Joint Conference on Artificial Intelligence, 9-14McSherry, D. and Stretch, C. (2005) Recommendation Knowledge Discovery. Proceedings of the 25th

SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence

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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

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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