In our experiments profile attributes have very little benefit if at all. Yes you can do that but you have to do use some advanced techniques to choose an LLR threshold or the model is likely (with default tuning values) to have a 100% density, meaning both genders like the item. This is an effect of the default tuning, which bypasses threshold calculation because it is no needed in most data but a Gender has only 2 possible values and the default tuning allows 50. Even if you said choose only 1, the difference in LLR score may be insignificant.
If you have a strong gender preference for items in your data it might be worth the t-digest & cross-validation tests but again in our experiments there are 2-3 very helpful secondary indicators and a whole lot of useless ones. Pick a few things that show a user’s taste, like search terms, browsing behavior (detailed product page views), along with your primary indicator and start there. Create a baseline cross-validation score with a gold-standard dataset. Then add to it to see if the score improves or not. You should A/B test even when cross-validation seem to improve. In several experiments it seems the more indicators you have the more you see diminishing returns. We got a 26% lift by using several indicators on the rottentomatoes movie review recommender but the las few only gave fractions of a %. 26% over using “like” alone. https://developer.ibm.com/dwblog/2017/mahout-spark-correlated-cross-occurences/ <https://developer.ibm.com/dwblog/2017/mahout-spark-correlated-cross-occurences/> On Dec 12, 2017, at 1:29 AM, Noelia Osés Fernández <no...@vicomtech.org> wrote: Thank you Pat! So if I'm understanding correctly, I could set a user profile property as follows: { "event" : "$set", "entityType" : "user", "entityId" : "u1234", "properties" : { "gender": "female" }, "eventTime" : "2015-10-05T21:02:49.228Z" } Although this is not recommended. Right? On 5 December 2017 at 17:38, Pat Ferrel <p...@occamsmachete.com <mailto:p...@occamsmachete.com>> wrote: The User’s possible indicators of taste are encoded in the usage data. Gender and other “profile" type data can be encoded a (user-id, gender, gender-id) but this is used and a secondary indicator, not as a filter. Only item properties are used a filters for some very practical reasons. For one thing items are what you are recommending so you would have to establish some relationship between items and gender of buyers. The UR does this with user data in secondary indicators but does not filter by these because they are calculated properties, not ones assigned by humans, like “in-stock” or “language” Location is an easy secondary indicator but needs to be encoded with “areas” not lat/lon, so something like (user-id, location-of-purchase, country-code+postal-code) This would be triggered when a primary event happens, such as a purchase. This way locaiton is accounted for in making recommendations without your haveing to do anything but feed in the data. Lat/lon roximity filters are not implemented but possible. One thing to note is that fields used to filter or boost are very different than user taste indicators. For one thing they are never tested for correlation with the primary event (purchase, read, watch,…) so they can be very dangerous to use unwisely. They are best used for business rules like only show “in-stock” or in this video carousel show only video of the “mystery” genre. But if you use user profile data to filter recommendation you can distort what is returned and get bad results. We once had a client that waanted to do this against out warnings, filtering by location, gender, and several other things known about the user and got 0 lift in sales. We convinced they to try without the “business rules” and got good lift in sales. User taste indicators are best left to the correlation test by inputting them as user indicator data—except where you purposely want to reduce the recommendations to a subset for a business reason. Piut more simply, business rules can kill the value of a recommender, let it figure out whether and indicator matters. And always remember that indicators apply to users, filters and boosts apply to items and known properties of items. It may seem like genre is both a user taste indicator and an item property but if you input them in 2 ways they can be used in 2 ways. 1) to make better recommendations, 2) in business rules. They are stored and used in completely different ways. On Dec 5, 2017, at 7:59 AM, Noelia Osés Fernández <no...@vicomtech.org <mailto:no...@vicomtech.org>> wrote: Hi all, I have seen how to use item properties in queries to tailor the recommendations returned by the UR. But I was wondering whether it is possible to use user characteristics to do the same. For example, I want to query for recs from the UR but only taking into account the history of users that are female (or only using the history of users in the same county). Is this possible to do? I've been reading the UR docs but couldn't find info about this. Thank you very much! Best regards, Noelia -- You received this message because you are subscribed to the Google Groups "actionml-user" group. To unsubscribe from this group and stop receiving emails from it, send an email to actionml-user+unsubscr...@googlegroups.com <mailto:actionml-user+unsubscr...@googlegroups.com>. To post to this group, send email to actionml-u...@googlegroups.com <mailto:actionml-u...@googlegroups.com>. To view this discussion on the web visit https://groups.google.com/d/msgid/actionml-user/CAMysefu-8mOgh3NsRkRVN6H6bRm6hR%2B1HuryT4wqgtXZD3norg%40mail.gmail.com <https://groups.google.com/d/msgid/actionml-user/CAMysefu-8mOgh3NsRkRVN6H6bRm6hR%2B1HuryT4wqgtXZD3norg%40mail.gmail.com?utm_medium=email&utm_source=footer>. For more options, visit https://groups.google.com/d/optout <https://groups.google.com/d/optout>. -- <http://www.vicomtech.org/> Noelia Osés Fernández, PhD Senior Researcher | Investigadora Senior no...@vicomtech.org <mailto:no...@vicomtech.org> +[34] 943 30 92 30 Data Intelligence for Energy and Industrial Processes | Inteligencia de Datos para Energía y Procesos Industriales <https://www.linkedin.com/company/vicomtech> <https://www.youtube.com/user/VICOMTech> <https://twitter.com/@Vicomtech_IK4> member of: <http://www.graphicsmedia.net/> <http://www.ik4.es/> Legal Notice - Privacy policy <http://www.vicomtech.org/en/proteccion-datos>
