BTW The Universal Recommender has it’s own community support group here: https://groups.google.com/forum/#!forum/actionml-user
From: Pat Ferrel <p...@occamsmachete.com> <p...@occamsmachete.com> Reply: user@predictionio.apache.org <user@predictionio.apache.org> <user@predictionio.apache.org> Date: May 11, 2018 at 10:07:25 AM To: user@predictionio.apache.org <user@predictionio.apache.org> <user@predictionio.apache.org>, Nasos Papageorgiou <at.papageorg...@gmail.com> <at.papageorg...@gmail.com> Subject: Re: UR: build/train/deploy once & querying for 3 use cases Yes but do you really care as a business about “users who viewed this also viewed that”? I’d say no. You want to help them find what to buy and there is a big difference between viewing and buying behavior. If you are only interested in increasing time on site, or have ads shown that benefit from more views then it might make more sense but a pure e-comm site would be after sales. The algorithm inside the UR can do all of these but only 1 and 2 are possible with the current implementation. The Algorithm is call Correlated Cross Occurrence and it can be targeted to recommend any recorded behavior. On the theory that you would never want to throw away correlated behavior in building models all behavior is taken into account so #1 could be restated more precisely (but somewhat redundantly) as “people who viewed (but then bought) this also viewed (and bought) these”. This targets what you show people to “important” views. In fact if you are also using search behavior and brand preferences it gets more wordy, “people who viewed this (and bought, searched for, and preferred brands in a similar way) also viewed” So you are showing viewed things that share the type of user like the viewing user. You can just use one type of behavior, but why? Using all makes the views more targeted. So though it is possible to do 1-3 exactly as stated, you will get better sales with the way described above. Using my suggested method above #1 and #3 are the same. 1. "eventNames”: [“view”, “buy”, “search”, “brand-pref”] 2. "eventNames”: [ “buy”,“view”, “search”, “brand-pref”] 3. "eventNames”: [“view”, “buy”, “search”, “brand-pref”] If you want to do exactly as you have shown you’d have to throw out all correlated cross-behavior. 1. "eventNames”: [“view”] 2. "eventNames”: [“buy”] 3. "eventNames”: [“buy”, “view”] but then the internal model query would be only the current user’s view history. This is not supported in this exact form but could be added. As you can see you are discarding a lot of valuable data if you insist on a very pure interpretation of your 1-3 definitions, and I can promise you that most knowledgable e-com sites do not mince words to finely. From: Nasos Papageorgiou <at.papageorg...@gmail.com> <at.papageorg...@gmail.com> Reply: user@predictionio.apache.org <user@predictionio.apache.org> <user@predictionio.apache.org> Date: May 11, 2018 at 12:39:27 AM To: user@predictionio.apache.org <user@predictionio.apache.org> <user@predictionio.apache.org> Subject: Re: UR: build/train/deploy once & querying for 3 use cases Just a correction: File on the first bullet is engine.json (not events.json). 2018-05-10 17:01 GMT+03:00 Nasos Papageorgiou <at.papageorg...@gmail.com>: > > > Hi all, > to elaborate on these cases, the purpose is to create a UR for the cases > of: > > 1. “User who Viewed this item also Viewed” > > 2. “User who Bought this item also Bought” > > 3. “User who Viewed this item also Bought ” > > while having Events of Buying and Viewing a product. > I would like to make some questions: > > 1. On Data source Parameters, file: events.json: There is no matter > on the sequence of the events which are defined. Right? > > 2. If I specify one Event Type on the “eventNames” in Algorithm > section (i.e. “view”) and no event on the “blacklistEvents”, is the > second Event Type (i.e. “buy”) specified on the recommended list? > > 3. If I use only "user" on the query, the "item case" will not be > used for the recommendations. What is happening with the new users in > that case? Shall I use both "user" and "item" instead? > > 4. Values of less than 1 in “UserBias” and “ItemBias” on the query > do not have any effect on the result. > > 5. Is it feasible to build/train/deploy only once, and query for > all 3 use cases? > > > 6. How to make queries towards the different Apps because there is > no any obvious way in the query parameters or the URL? > > Thank you. > > > > *From:* Pat Ferrel [mailto:p...@occamsmachete.com] > *Sent:* Wednesday, May 09, 2018 4:41 PM > *To:* user@predictionio.apache.org; gerasimos xydas > *Subject:* Re: UR: build/train/deploy once & querying for 3 use cases > > > > Why do you want to throw away user behavior in making recommendations? The > lift you get in purchases will be less. > > > > There is a use case for this when you are making recommendations basically > inside a session where the user is browsing/viewing things on a hunt for > something. In this case you would want to make recs using the user history > of views but you have to build a model of purchase as the primary indicator > or you won’t get purchase recommendations and believe me recommending views > is a road to bad results. People view many things they do not buy, putting > only view behavior that lead to purchases in the model. So create a model > with purchase as the primary indicator and view as the secondary. > > > > Once you have the model use only the user’s session viewing history in the > as the Elasticsearch query. > > > > This is a feature on our list. > > > > > From: gerasimos xydas <gerasimos.xy...@gmail.com> > <gerasimos.xy...@gmail.com> > Reply: user@predictionio.apache.org <user@predictionio.apache.org> > <user@predictionio.apache.org> > Date: May 9, 2018 at 6:20:46 AM > To: user@predictionio.apache.org <user@predictionio.apache.org> > <user@predictionio.apache.org> > Subject: UR: build/train/deploy once & querying for 3 use cases > > > > Hello everybody, > > We are experimenting with the Universal Recommender to provide > recommendations for the 3 distinct use cases below: > > - Get a product recommendation based on product views > - Get a product recommendation based on product purchases > - Get a product recommendation based on previous purchases and views (i.e. > users who viewed this bought that) > > The event server is fed from a single app with two types of events: "view" > and "purchase". > > 1. How should we customize the query to fetch results for each separate > case? > 2. Is it feasible to build/train/deploy only once, and query for all 3 use > cases? > > > Best Regards, > > Gerasimos > >
