Conversational and Critique-Based RecommenderSystems

Jitendra Kasaudhan

ga35mav@mytum.de

ABSTRACTRecommender system offers more personalized and respon-sive search system to find the specific products in an e-commerce environment. It merges the idea of user profilingand preferences, interactive and adaptive interfaces, user’sfeedback, rating, review etc. Critique based conversationalrecommender system uses user’s feedback called critiquesmade on particular feature of the product and recommendsmore accurate suggestions on the subsequent iterations. Forexample while shopping for a laptop, user might specify”higher processing speed”which is a critique over the featurecalled ”speed” of the laptop. Most of the time , it is usefulto specify critiques over multiple features of the product.Conversational recommender system also solves the prob-lem of product recommendation which does not have initialuser rating.This paper will explain the three major branchesof critique-based conversational recommender systems, nat-ural language based, system suggested and user- initiatedcritiques systems. Conversational recommender system alsosolves the problem of product recommendation which doesnot have initial user’s rating.Each system will be presentedalong with the example. Similarly, advantages and disad-vantages for each branch will be analysed and discussed.Since all these respective fields have some disadvantages, ahybrid framework has been developed which unifies advan-tages of different methods and effectively enable users toachieve more confident decision while searching for a prod-uct.

1. INTRODUCTIONRecommender systems assist users to identify an ideal

product which satisfies their needs and develops confidenceon selecting the recommended items. It combines the con-cept of user profiling and preferences, machine learning, user’sfeedback etc to provide them an efficient searching platformcapable of searching preferred product easily and quickly.According to a usability researcher, Jacob Neilsen, ”if userscannot find the product they cannot buy it as well”. There-

fore, recommender system can play a vital role in findingthe actual product in an e-commerce environment. Critiquebased conversational recommender system can behave likean artificial salesperson who recommends products based onuser’s initial preferences and elicits the feedback on each it-eration and recommends improved and more specific results.The feedback from the user called critiques is used on eachrecommendation cycle to improve the result. For exampleuser might specify ”higher memory” which is a critique fora feature over RAM of the Laptop, which is an exampleof unit critique over a single feature. Critiquing based rec-ommender system usually uses fixed style of user interactionby presenting fixed set of standard recommendations in eachrecommendation cycle. It is an incremental process to builduser preferences and provide them more accurate and confi-dent decisions even in complex decision environment.

Critiquing based conversational recommender system fol-lows system-user interaction model as shown in the Figure1

Figure 1: User preference interaction model[5].

2. TYPES OF CRITIQUING BASED CON-VERSATIONAL SYSTEM

2.1 Natural language dialogue based systemHelping customers to access the information successfully

in an e-commerce environment accommodate the need ofboth customer and business requirements. Natural languagebased dialogue system provides faster and more intuitiveway of human and computer interaction in a commercialenvironment. In an e-commerce platform, natural languagedialogue system appears to be effective means of negotiat-ing user’s request and intentions. Research from the IBMT. J. Watson Research Center[4] has proposed system ar-chitecture for natural language dialogue system which canbe implemented in any kind of e-commerce platform. It hasthree major components, Presentation Manager, Dialogue

Manager and Action Manager as shown in the figure 2. Pre-sentation manager is responsible for interaction with userusing text, mouse, audio or keyboard inputs which is intu-itively presented to gather the user preferences. DialogueManager applies dialogue strategic rules based on the infor-mation state ,user preferences and results from the ActionManager. Action manager is responsible for making actualsql queries from the databases.

Example of natural dialogue system is an ExpertCleck.

2.1.1 ExpertClerkExpert clerk is a virtual system which act as a human

salesperson and interact with the buyers in natural languageto confine their search by asking them relevant questionswhich is also termed as Navigation by asking. Expert clerkthan proposes the options which is based on initial user pref-erences and continues the interaction cycle until user findsthe required product. Example below shows how IBM vir-tual clerk interact with users and help them to find the rightproduct[3].

NLA : What type of ThinkPad are you looking for?User : ”Something with at least 256 mb ram.”NLA : ThinkPads ship with memory between 64 and 128MB. I could not find any ThinkPads. Here are some popu-lar modules. What are your price constraints?User : [presses Start Over button] ”A windows 2000 com-puter with a battery life of at least 3 hours.”NLA : I found 18 ThinkPads for you. What are your speedand RAM requirements?User : ”At least 128 mb ram with multimedia features.”NLA : I found 2 ThinkPads for you. What are your priceconstraints?User : ”The lightest one.”NLA : I recommend the ThinkPad model T23 for you.

Figure 3: User specifying their requirement in Nat-ural language dialogue based recommender systemfrom IBM [3].

In the example in figure 3 user specifies ”at least 128 mbram” and the system proposes ThinkPad with ram less than128 mb. Likewise, user also specifies battery life to be ”atleast 3 hours” and operating system to be windows 2000 sothe recommender system understands it and shows a list ofproducts having battery life more than 3 hours and finallythe required product.

2.1.2 Advantages and Disadvantages

Figure 4: Think Pad assistant of IBM recommend-ing product based on user’s input [3].

Natural language dialogue system narrow down the userinteraction cycle helping them to find the right product morein a user friendly and smart way.It is useful for the userswho does not have enough idea about what exactly to pur-chase,and the system can guide them as a personal assis-tant. It has potential of converting visitors into potentialcustomers in various e-commerce platforms like selling shoesonline, fashionable products, finding apartments etc.

However, it requires concise natural language processingskill and high involvement of user , so it cannot be under-taken as an ideal case for serving online users in e-commerceplatform. User might be able to find the required productwithin single interaction or might not find after numerousinteractions. It can be time consuming process for the userwho knows the exact details of the product he/she want topurchase.

2.2 System suggested critique systemIt generate the recommendations based on the knowledge

based critiques like product specific information, user’s cur-rent preferences, availability of the product etc. It helps userto search through massive multi dimensional informationsystem using the product domain knowledge and critiqueslike ”cheaper”, ”highest speed”, ”less weighty” etc. Variousapproaches have been applied to improve system suggestedrecommendations which can be described below.

2.2.1 Dynamic critiquingDynamic critiquing allows users to select multiple cri-

tiques over the multiple features of the product simultane-ously. Instead of detail selection of features, user prefer di-rectional preference of the feature like ”the lightest laptop”or ”the cheapest computer” etc. Dynamic critiquing allowsmore sophisticated methods to include the factors like diver-sity of the product during the information retrieval process.Dynamic critiquing applies several approaches to automat-ically generate and prioritize compound critiques which isimportant because fixed critiques for the feature of the prod-uct might not be relevant for the user in the some context.For example, if buyer is searching for a car for personal use,”sporty” can be a critique but if he is searching for familythan it might be appropriate to show ”less fuel consump-tion cars” ,”lower engine size” as a critiques. Since the setof pre-designed critique suggestions and unit critiques oversingle feature of the product at a time were not efficient tocater the changing needs of the user, Reilly et al. (2004)[7]

Figure 2: System architecture of natural language based dialogue system [4].

proposed a strategy to incorporate multiple unit critiquesover multiple features which is called dynamic compoundcritiques.

The process of compound critiques identification dependson finding useful recurring subset of critiques from the collec-tion of critique patterns called pattern- base[2]. Applicationof critiques over the single features is the easiest way to usebut there exist a combination of the critiques which is hardto refine after each recommend-review and revise interactioncycle. For example, there can be combined critiques with[Monitor Size <] but [Hard-Disk >] over the feature screensize and hard drive space. It is infact a challenging problemto identify the recurring combined critiques, because of theinvolvement of the several thousands of different productsin an e-commerce platform.

Therefore a rule mining algorithm called Apriori algo-rithm is used to narrow down the search space of the pos-sibilities so that only the subset of the possible compoundcritiques is checked. Apriori algorithm characterises the re-curring item as an association rule of the form A -> B .Itmeans, with the presence of a certain set of critiques (A), wecan infer the presence of other critiques (B). For example,from the presence of the critique, [Monitor <], we can inferthe presence of [Hard-Disk >] with a high degree of prob-ability to support the compound critique pattern [Monitor<],[Hard-Disk >]. Apriori measures the importance of a rulein terms of its support and confidence value. Support valuerefers to the percentage of the product that satisfies the cri-tiques for example, we can find that the rule [Monitor <]-> [Hard-Disk >] with a support value of 0.1, if there area total of 100 critique patterns but only 10 of them contain

[Monitor <] and [Hard-Disk >].According to the live-user trial research (with 38 subjects)

(McCarthy et al. 2005b,c)[6], total number of interaction cy-cled decreased from 29 to 6 when users actively selected thecompound critiques. Similarly, an extended version calledincremental critiquing was used to record the history ofuseraAZs critiques, so that system could avoid repeated rec-ommendation of attribute value(s) which was already dis-liked by user (Reilly et al. 2005b)[7]. This approach showedthat the user interaction was reduced by 34 percent withrespect to the original recommendation system. Figure 5shows an example of unit and compound critiques.

2.2.2 MAUT based compound critiques and visualcritiquing

Dynamic critiquing does not care about the user’s interestin the system suggested critiques so Zhang and Pu (2006)[11]proposed a method for adapting the generation of compoundcritiques according to the user preferences using multi at-tribute utility theory(MAUT). Based on the user’s currentpreferences, top k products with maximal MAUT value isretrieved during each recommendation cycle. Compared todynamic critiquing, MAUT was proven to have a higherquality of recommendation which could match the user’s in-tended critiquing criteria. For proper usability, Zhang et al.(2008)[11] modified their user interface by including visualdesign with meaningful iconic images as a replacement oftextual critiques as shown in the figure 6. Different colorcodes were used for representing critiques like slower CPUand smaller screen size were negative(red color) and higherattributes were marked green.

Figure 6: Example of Compound critiques original textual interface (above) and its new visual interfacedesign (below) (Zhang et al. 2008)[11]

Figure 5: Web interface showing dynamically gen-erated unit and compound critiques.

2.2.3 Preference based organisation interfaceCritiques proposed by MAUT based system was confined

only for single product so that many recommendations werenot possible and each critique would contain too many at-tributes which could cause information overload. Therefore,preference based model comes into play which not only gen-erates dynamic critiques based on user’s MAUT value pref-erence model but also applies Apriori algorithm to generatecompound critiques. Algorithm for preference based organi-sation contains three major steps(Chen and Pu 2007c)[1] (a)It gathers user initial preference as a weighted sum of MAUTvalue. (b) Based on the user’s preference model, it generatesrecommendations and converts them into a tradeoff vectorexcept the top candidate. Trade off vector is a set of (at-tribute, tradeoff) pairs which is compromised or improvedproperty of the product’s attribute value compared to thesame attribute of the top item.(c) Finally, it uses Apriorialgorithm to find the recurring subset of the tradeoff vector(attribute tradeoff) pairs as a system suggested critiques.

Figure 8 shows a list of recommendations suggested bypreference based organisation system where the top mostrow is the most probable user’s intended preference andthe remaining system suggested compound critiques are di-versified results for example [cheaper and lighter but fewermegapixel ], [more resolution, more screen size but expen-sive] etc.

2.2.4 Discussion: Advantages and Disadvantages

System suggested critiques provide guidance for user toprovide feedback so that the system can solicit their interestand generate valuable recommendations. If the proper setof critiques are presented based on user’s preferences, it canhighly decrease the interaction effort and increase the de-cision performance. Likewise, suggested unit or compoundcritiques can better explain the background logic behind therecommendation which increase user confidence on trustingthe system.

According to the research (Chen and Pu 2007c)[1], cri-tique prediction accuracy was limited to 66.9 percent usingpreference based organisation interface. Since user are notable to build the critiques of their own, in some cases theymight have to go through the long interaction cycle to findthe required product. As for example shown in figure 8,if user wants to find a camera with higher resolution andhigher optical zoom, which is not suggested by the system,it might take enormous effort for user to find the right prod-uct and it is most likely that the user would prefer to leavethe system.

Figure 7: User initiated critiquing interface whereuser can improve the search by specifying differentvalues of tradeoff parameters[5] [10].

2.3 User initiated critiquing systemIt allows user to control the concrete critiquing tradeoff

criteria. Rather than suggesting critiques for user , they canselect either unit or compound critiques with complete free-dom over several features of the product using user initiatedcritiquing approach. As shown in the figure7 ,user can man-

ually specify the values of trade off attributes which providemore comfortability from the perspective of user.

2.3.1 Example critiquingExample critiquing is the process where the system pro-

poses a set of possible solutions according the the user’scurrent preference model and user interact either by pickingone item or modifying the values of this preferences.

Once the user have specified their preferences , weightof improved attribute is increased while compromised at-tribute is decreased. Based on MAUT values of attributes(eg .flight finding system), search engine will deploy con-straint satisfaction algorithm which include both hard con-straints (which cannot be violated ) or soft constraints. Con-straints satisfaction problem requires optimally preferred so-lution so pareto optimality can be applied to compute theoptimum result.According to the research conducted by (Puand Chen )[1] , example critiquing system were able to in-crease user’s decision accuracy, save their cognitive decisioneffort and improve their decision confidence and preferencecertainty.

2.3.2 Discussion: Advantages and DisadvantagesUsing user initiated critiques approach, user can create

their desired critiques whereas in system suggested approachthey can only select the critiques. Beside it, user initiatedcritiquing system provide better option to specify more cri-tiques and control trade off parameters facilitating improve-ment in user’s decision accuracy and confidence.

Even though , user initiated approach reduces user’s cog-nitive effort during navigation, interaction effort is nearlyequivalent or higher than the system suggested critiquingapproach. Likewise, studies(Chen and Pu 2006)[1] showsthat , the user required a warm up time to grasp the userinitiated critiquing facilities as it was a bit complex thansystem suggested critiquing system.Thus, this system is notable to reduce the interaction effort maintaining higher levelof user control.

3. HYBRID SYSTEMBased on the experiment conducted by (Pu and Chen

2005)[8], example critiquing was found better than dynamiccritiquing in terms of user’s decision confidence, accuracyand effort consumption. Two different forms of hybrid sys-tem have been developed which combines advantages of sys-tem suggested and user initiated critiquing system.

3.1 Hybrid critiquing system v1 (System sug-gested plus User initiated)

As discussed previously, 36.1 percent (Chen and Pu 2006)[9]user preferred dynamic critiquing system because it wasintuitive and straightforward for collecting compound cri-tiques and encouraging them to think about the tradeoffcriteria but it did not allowed user to create their own cri-tiques. To overcome this problem, example critiquing sys-tem was developed to allow the user to create their own cri-tique and provide higher level of user control. Therefore anew hybrid system was developed which include both systemsuggested dynamic compound critiques and a control panelto create the critiques like example critiquing as shown in 9.This hybrid system provided higher level of user control anddecision perception along with minimized interaction effort.

Figure 9shows the recommended product on the top andthe middle section include system suggested compound cri-tiques and the bottom section include self motivated cri-tiquing area with different types of critiquing modality.Inthis kind of interface user can easily specify camera withspecfic values of resolution and optical zoom if the suggestedoptions are not appropriate.Based on the critiques suupliedby user, system deploys search alogorithm. For example,if the selected critique is dynamic critique , it uses similar-ity and compatibility selection approach and deploys elim-ination by aspects (EBA) and weighted additive sum rulemethods.

Figure 10: Hybrid system v2 (Preference based plusexample critiquing system)

3.2 Hybrid critiquing system v2 (Example cri-tiquing plus Preference based )

As mentioned previously, preference based organisationinterface focuses more on user preference along with the ap-plication of the mining rule while dynamic critiquing is moredependent on fixed set of data. For example , dynamic cri-tiquing could suggest ”cheaper and more memory” critiqueswhich fixed defined weight for each attribute ( price , mem-ory) but with preference organisation system, user’s selec-tion is considered to change the weight of the attributes so asto generate the best results.Since, preference based organisa-tion provided higher critique prediction accuracy and exam-ple critiquing system had higher decision accuracy and con-fidence ,a new hybrid system was developed combining ex-ample critiquing and preference based critiquing approach.

Figure 10 shows the hybrid system where user is recom-mended with the best suited product as in preference basedorganisation with an option called ”self specify your own cri-tiquing criteria- Better Feature” which will display examplecritiquing interface as shown in Figure(b). Each preferencecontains (attribute value, weight) pairs of participating at-tributes to compute MAUT value. Once the user have spec-ified their initial preference, best suited result is displayedon the top along with other products on the bottom sectionbased on preference organisation algorithm.If user wants to

Figure 8: The preference-based organization interface where category titles behave as suggested compoundcritiques (Chen and Pu 2007c)[1]

Figure 9: Hybrid system version 1 showing recommended product along with system suggested and userinitiated critiques[5].

Figure 11: Example critique system used by store.hp.com, amazon.com and newegg.com

query better result on each recommendation they could use”Better Feature” option as shown in figure 10, [5]

3.3 Survey on how conversational features areused in current applications

Most of the current e-commerce applications are devel-oped to assist the users who are aware of what they are pur-chasing. List of features of product along with the critiquesassociated with it are displayed and user can have fine con-trol over the attributes of the product he/she wants.Figure11 shows the common patterns of example critiquing systemused by store.hp.com, amazon.com, newegg.com. System al-lows user to specify critiques within a range like screen sizebetween 14 inch to 16 inch and also provides user friendlysliding interface for specifying parameter like price. Thisapproach is not useful for the people who do not have suf-ficient knowledge and confidence of what they are trying topurchase.

Similarly, in hp store user can initiate comparison pro-cess by picking up different products (as shown in the figure12 )and compare same features side by side which providesgood overview and helps in the decision process.But it dealswith enormous user cognitive effort and requires sufficientknowledge for purchasing.

Online platforms like http://www.deere.com/ uses natu-ral language dialogue based assistant which goes through theseries of questions and grabs user preferences in the form ofcheckboxes and text inputs.

4. CONCLUSION

This paper explains different approaches, experiments andefficiencies of various conversational recommenders systemsused in e-commerce platform. Natural language dialoguebased system proved to be a virtual sales person to suggestproducts based on the user’s preferences but it required enor-mous amount of natural language processing skills and highinvolvement of the user.Similarly, system generated com-pound and unit critiques were used to refine the search cri-teria and increase user confidence with minimum interactioneffort.Compared to dynamic critiquing, MAUT was provento have a higher quality of recommendation which couldmatch the user’s intended critiquing criteria.Likewise, Apri-ori algorithm was used to generate compound critiques fromthe recurring subset of tradeoff vectors and displayed the re-sults in an organised interface. Critique prediction accuracywas limited to 66.9 percent using this approach (Chen andPu 2007c)[1]. So, user initiated critiquing system was devel-oped which could provide higher level of user control. Usingexample critiquing system, user can create their own cri-tiques and specify tradeoff parameters which supports user’sdecision accuracy but it required initial learning time to un-derstand the system.Since, all of the approaches describedabove had some cons, hybrid system were proposed combin-ing System suggested plus User initiated and Example cri-tiquing plus Preference based.Hybrid system were successfulin attaining maximum efficiency in terms of user’s decisionaccuracy, confidence and effort.

5. REFERENCES[1] P. P. Chen, L. Preference-based organization

Figure 12: Comparison of products over same features

interfaces: aiding user critiques in recommendersystems. 2007.

[2] L. M. James Reilly, Kevin McCarthy and B. Smyth.Dynamic critiquing.

[3] N. N. M. S. N. K. W. Z. Joyce Chai, Veronika Horvath

and P. Melville. Natural language assistant aAS adialog system for online product recommendation.

[4] S. G. V. H. N. N. . W. Z. Joyce Chai,Margo Budzikowska. Natural language sales assistant.2010.

[5] P. P. Li Chen. Critiquing-based recommenders: surveyand emerging trends. 2011.

[6] M. L. S. B. R. J. McCarthy, K. On the evaluation ofdynamic critiquing: a large-scale user study. 2005b.

[7] R. J. M. L. S. B. McCarthy, K. On the dynamicgeneration of compound critiques in con- versationalrecommender systems. 2004a.

[8] C. L. Pu, P. Integrating tradeoff support in productsearch tools for e-commerce sites. 2005.

[9] C. L. Pu, P. Trust building with explanationinterfaces. 2006.

[10] F. B. P. P. Torrens, M. Smartclients: constraintsatisfaction as a paradigm for scaleable intelligentinformation systems. 2002.

[11] J. N. P. P. Zhang, J. A visual interface forcritiquing-based recommender systems. 2008.