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- 2 - Basic I/O Relationship Knowledge-based: "Tell me what fits based on my needs"

- 3 - Knowledge-source of different types of recommender systems  Recommender systems are differentiated based on their adopted knowledge source –Collaborative Filtering  Users ratings  Demographic information –Content-based Filtering  Item features  Keywords for textual items –Knowledge-based systems  Knowledge-model

- 4 - Why do we need knowledge-based recommendation?  Products with low number of available ratings  Time span plays an important role –five-year-old ratings for computers –user lifestyle or family situation changes  Customers want to define their requirements explicitly –"the color of the car should be black"

- 5 - Knowledge-based Recommender systems  Defined as a System which “Guide users in a personalized way to interesting objects in a large space of possible options” [Bruke 2000] –Rely on detailed knowledge about item’s features  e.g. item catalog for the digital camera (go to next slide!) –Customers interactively share their requirements with the recommender  e.g. the price should be lower than 300 –Recommendation problem is selecting items from the catalog that matches users need  Recommendation are calculated independently of users ratings: –Either in the form of similarities between customer’s requirement and items –Or explicit recommendation rules  Advantages: –No ramp-up problem (NO users rating are required)

- 6 - Example of product assortment: digital cameras(Felfernig.2009)

- 7 - Different Types of Knowledge-based recommender systems  Constraint-based –retrieved items based on explicitly defined set of recommendation rules –fulfill recommendation rules  Case-based –retrieve items that are similar to specified requirements – based on different types of similarity measures  Both approaches are similar in their conversational recommendation process –users specify the requirements –systems try to identify solutions –if no solution can be found, users change requirements –Systems also provide explanations for the recommended items

- 8 - Constraint-based recommender systems: CSP  Constraint satisfaction problem (CSP), described by a tuple (V,D,C): –V is set of variables –D is set of finite domains for V –C is constraints that describes the combination of values and variables can simultaneously take  Solution to CSP corresponds to an assignment of a value to each variable in V so that all constraints are satisfied. –Accomplished by standard constraint solver

- 9 - Constraint-based recommender systems: CSP  Knowledge base of Constraint-based recommender systems –Variables  Consumers requirements (V C ), item features (V PROD )  V= (V c U V PROD ) –Sets of constraints  Filter constraints (C F ): IF user requires A THEN proposed item should possess feature B  Compatibility constraints (C R: ): IF user requires A1 THEN she should propose A2>200  Product constraints(C PROD ): defines available product assortments and their features  Customers query (REQ) encoded as a constraints over the variables V c and V PROD

Example  Select items from this catalog that match the user's requirements  User's requirements (V C ) can, for example, be –"the price should be lower than 300 €" –"the camera should be suited for sports photography" idprice(€)mpixopt-zoomLCD-sizemoviessoundwaterproof P1P × 2.5no yes P2P × 2.7yes no P3P × 2.5yes no P4P × 2.7yesnoyes P5P × 3.0yes no P6P × 3.0yes no P7P × 3.0yes no P8P ×3.0yes Table 2

Example Example of recommendation task (V C,V PROD,C R,C F,REQ) and the corresponding recommendation results (RES )

Constraint-based recommendation tasks: CSP  Recommendation task: identifying a set of product matching (RES) based on users requirements (REQ). –Find a set of user requirements such that a subset of items fulfills all constraints  ask user which requirements should be relaxed/modified such that some items exist that do not violate any constraint –Find a subset of items that satisfy the maximum set of (weighted) constraints, considering REQ  All proposed items have to fulfill the same set of constraints –Rank items according to weights of satisfied constraints, based on REQ  rank items based on the ratio of fulfilled constraints

Constraint-based recommender systems : conjunctive query  View item selection problem as a data filtering task –V PROD represents Table attributes and C PROD represents table entries  set of available items in a table :{p1,p2,..., p8} (e.g. Table2) –Construct conjunctive database query based on user requirements (REQ), and executes against Table 2  e.g. σ[mpix≥10, price<299](P) = {p4, p7}  Conjunctive query is an integration of –Filter condition (C F )  e.g. “usage=large-print  mpix>5.0” –User requirements (REQ)  e.g. “Usage = large-print, price <300”  If user requirements denotes “usage = large-print”, the consequent part of condition (mpix >5.0) will be integrated in conjunctive query –e.g. σ[Usage = large-print, mpix≥5, price<300](P)

 σ[mpix<4,Usage = large-print, price<299](P) ???

Interacting with constraint-based recommenders 1.The user specifies his/her initial preferences e.g. by using web-based form –all at once or –incrementally in a wizard-style –interactive dialog 2.The user is presented with a set of matching items –with explanation as to why a certain item was recommended 3.The user might revise his or her requirements –see alternative solutions –narrow down the number of matching items

Interacting with constraint-based recommenders: Defaults  Support customers in the requirements specification process in a situation where users –users know what they want but unsure about which option to select –simply do not know technical details  Type of defaults –static defaults  One default value is specified for each customer property, e.g. “default (usage) = large-print” –dependent defaults  Default is defined on different combinations of customer requirements, e.g. “default(usage = small-print, max-price =300)” –derived defaults  Exploit existing interaction logs to derive default values (Example in next slide)

 Derived defaults can be determined based on two schemes: –1-nearest neighbors  Predict the default value for one or sets of users’ requirement  Select the entry of the interaction log that is most similar to user’s requirement  e.g. if user’s REQ={Price =400, opt-zoom =10X}, the default value for lcd-size is 3.0 –Weighted majority voter  Propose default value for customer property based on the voting of a set of similar items for that specific property, e.g. if REQ ={150 <=price <=200}, default value for lcd-size is 2.7 Defaults Interaction logs of different customers

Defaults  Selecting the next question –most users are not interested in specifying values for all properties –Default should identify properties that user may be interested to ask next –Question recommendation based on the principle of frequent usage (popularity)  How often the specific property has been previously asked by other users, according to interaction log –e.g., popularity (price, pos:1) =0.6, popularity (mpix,pos:1) = 0.4  first question is about price

Constraint-based Recommender systems : etown’s Ask Ida  No longer exists (old screenshots)  Uses an interview process to elicit user preferences to recommend products  Uses recommendation as a point to elicit further preferences

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 What does system do when no recommendation found???????  Dealing with the unsatisfied requirements!

Interacting with Constraint-based Recommender: Ranking the items  Rank the item based on Multi-attribute utility theory (MAUT) –each item is evaluated according to a predefined set of dimensions (i.e., quality, economy) that provide general perception on the basic item properties.  E.g. quality and economy are dimensions in the domain of digital cameras  e.g. price <=250, the score of quality dimension is 5, the score of economy is 10 idvaluequalityeconomy price ≤250 > mpix ≤8 > opt-zoom ≤9 > LCD-size ≤2.7 > movies Yes no sound Yes no waterproofYes no Example of scoring rules regarding the quality and economy dimensions

Item utility for customers  Utility function  Customer specific interest (preferences)  Calculation of Utility for customers cu1 and cu2 based on the utility function over different dimensions Customerqualityeconomy Cu 1 80%20% Cu 2 40%60% Contribution (p,quality)Contribution (p,economy) Utility(p) for cu 1 Utility(p) for cu 2 P 1 Σ(5,4,6,6,3,7,10) = 41Σ (10,10,9,10,10,10,6) = [8]55.4 [6] P 2 Σ (5,4,6,6,10,10,8) = 49 Σ (10,10,9,10,7,8,10) = [7]58.0 [1] P 3 Σ (5,4,10,6,10,10,8) = 53 Σ (10,10,6,10,7,8,10) = [5]57.8 [2] P 4 Σ (5,10,10,6,10,7,10) = 58 Σ (10,6,6,10,7,10,6) = [4]56.2 [4] P 5 Σ (5,4,6,10,10,10,8) = 53 Σ (10,10,9,6,7,8,10) = [6]57.2 [3] P 6 Σ (5,10,6,9,10,10,8) = 58 Σ (10,6,9,5,7,8,10) = [3]56.2 [5] P 7 Σ (10,10,6,9,10,10,8) = 63 Σ (5,6,9,5,7,8,10) = [2]55.2 [7] P 8 Σ (10,10,10,9,10,10,10) = 69 Σ (5,6,6,5,7,8,6) = [1]53.4 [8]

Case-based recommender systems  Earlier version of case-based recommender –Followed Query-based approach  customers have to specify their requirements and system reterive items similar to users requirement –drawback: customers maybe not know what they are seeking –Solution: development of the browsing-based approach  Users who do not know what they are seeking, are navigating in the item space with the goal to find useful alternatives.  State-of-the-art of case-based recommender (aka, critique-based recommender) –integrates query-based approach with browsing-based approach  Support the identification of the most similar items  Allow users to change preferences without being force to specify the exact value for item attributes.

Similarity measures in Case-based recommender systems  Items are retrieved using similarity measures that describe to which extent item properties matches user’s requirements  Distance similarity  Def. –sim (p, r) expresses the distance of user requirement r ∈ REQ from each item attribute value φ r (p).  e.g. sim(p 1, r 1 = mpix = 10): shows a distance of the item attribut φ r = mpix (p 1 ) = 8.0 from user requirement, r 1 = mpix =10. –w r is the importance weight for requirement r

Similarity measures in Case-based recommender systems  In real world, customer would like to –maximize certain properties. i.e. resolution of a camera, "more is better"(MIB)  The local similarity measure of requirement r and item p is calculated as, –minimize certain properties. i.e. price of a camera, "less is better"(LIB)  The local similarity measure of requirement r and item p is calculated as, – there are situations in which the similarity should be based solely on the distance to the originally defined requirements (i.e., certain run time or monitor size )

Interacting with case-based recommenders  Critiquing is an effective way to support such navigations (browsing) –Customers specify their change requests (e.g. price or mpix) that are not satisfied by the current item (entry item or recommender item) –The goal of critique-based recommender is to achieve time savings in item selection process as well as to achieve the same recommendation quality as query-based approaches Critique on price

Unit Critiquing Algorithm SimpleCritiquing algorithm

Compound critiques  Operate over multiple properties can improve the efficiency of recommendation dialogs (e.g., cheaper and more pixel) –Reduce the number of critiquing cycles –Faster progress into item space

 Item recommendation based on –Similarity degree of new recommended item (c i ) with user requirement (r i ) –Compatibility degree of recommended item (c i ) with the already selected compound critique (CC U )  Quality measure for the recommended item (c i ) Item recommendation in case-based recommenders

Case-based recommender systems: Example  Entrée (Bruke 2000), a restaurants recommender systems developed based on critique-based recommendation approach –Users interact with Entrée using web-based interface –the goal of Entrée is identifying a restaurant that fits user needs –Entrée recommender use FindMe technologies(Burke,1997)  Implement the idea of critique-base recommender, allow navigation in complex item space  Follow the static critique approach  Two-entry points to Entree –Use a reference restaurant –Specify the requirements of users (price, cuisine type, noise level)

Entrée recommender (interface)

Entrée recommender…

Summary  Knowledge-based recommender systems –constraint-based—(query-based approach) –case-based—(integration of query-based and browsing-base approaches)  Limitations –cost of knowledge acquisition  from domain experts (determination of the recommendation rules/constraints)  from users(determination of user requirements) –accuracy of preference models  very fine granular preference models require many interaction cycles –independence assumption can be challenged  preferences are not always independent from each other