Welcome back. In this video we have with us Dr. Bamshad Mobasher, who is a faculty member at DePaul University and has done a variety of recommender systems research projects over the years, including a number on context-aware recommendation. He's also involved in the ongoing leadership of the ACM Conference on recommender systems, where you can find a lot of current research on many aspects of recommendation. Barnshad, welcome to the class. >> Thank you. >> So, we've talked a little bit in this class about, that there exists a concept of context, and that it makes a difference for our recommendation. What is context and how can we take it into account when we're trying to provide recommendations to users? >> Okay, well, let's just begin with the way that context is emerging in a lot of different applications that are being used in information access. So we are seeing that increasingly, As people access more complex types of information environments, where there's lots of different types of items, or there's lots of complexity in the structure of the types of navigation that can occur in these system, there's more need for guidance. And, of course, this is sort of a general reason behind having more information filtering in, recommender systems are a part of that. But one thing that people are seeing is that when you're searching for items, or when you're trying to navigate and access the right piece of information, often your preferences is just part of the picture. There are other aspects of your interaction, either with the environment or with the system, that are relevant that could be useful as part of sort of focusing and honing in on the right piece of information. Just what you see in here, for example, is Yelp, the kind of things that you might be looking at when you're searching let's say for a restaurant. Obviously, in this case, it's in a particular location. But if you look at the bottom in here, you can see that you have specific selections for different neighborhoods. You have things like this is good for kids or good for groups. So these are elements that go beyond not just general preferences but also take into account the current situation of the user as well. Just to extend that example a little bit let's take a look at say Spotify, or your typical music streaming service or recommender system. And you have typically things like genres, artists, albums, etc. But as you can see in a lot of these applications, the kinds of things that sort of take into account the user's current environment or situation also are coming in, like the user's mood, for example. Are you in a good mood or a bad mood or a sad mood or a happy mood, et cetera? Are you doing a workout or are you, let's say, studying or relaxing, or something along those lines? Is this music that might be good for traveling? So a lot of these things that you see in these systems are trying to take that situational awareness into account. In the context of recommendation, context also that's coming in, and part of that is predicated on the assumption that your preferences might change if you go from one situation to another situation. So, for instance, if I'm the movie, Frozen, for example, I may just dislike it if I'm trying to watch a movie by myself so I may not rate that very highly. But, if I'm watching it with kids, you know, then maybe that's a good movie for that particular context, and in that context I may rate it highly. So this sort of points to the idea that you may have different preferences or even different ratings for the same item but in different contexts. So let me just get to this couple of definitions of context. By the way, there are lots of definitions of context. You can find literally thousands of definitions of context. And I know because I tried earlier, and then you know they come from lots of different domains, so that's a part of it. It's not all in for recommendation. But I picked a couple of them because they sort of underlie the two general directions that people have looked at context in computing, both in ubiquitous computing and mobile computing. And nowadays in recommendation. So one of them is this definition by Dey, and some of his colleagues, which basically says that context is any information that can be used to characterize the situation of an entity. So, this points to the idea of situated actions. That actions of an entity and agents are relevant in a particular situation, and the same actions maybe, for example, have a different relevance, or utility, in a different kind of a situation. So this says something about context having something to do with the interaction of user with the environment. The other definition that kind of points to sort of the other side of the spectrum on how people view context is this one by Lieberman and Selker back in 2000, which says that context is everything that affects computation except its explicit input and output. So it's pretty general, but it does suggest that context is something that's independent and outside of the entities actions. So that's to some extent this is in contradiction to the other definition that I just pointed to. But you really get at two separate aspects of context and both of which have sort of found their way into various systems. You can still sort of think about the first definition saying that context is something that is intentional that is not observable directly but it's part of interaction. And the second definition sort of saying that context is extensional. That means there is some specific set of factors, or whatever, outside of just the normal input and output that independently describe what context is. So this led to debates about context in computing, and famously Paul Dourish, 2004, distinguished there are two views of context along these two lines that I was just describing. One was referred to as representational view, which says that context basically can be represented explicitly using some sort of variables that are observed and are distinguishable from the underlying activity. So things like location and time, very sort of things that you can actually point to and measure. The other view is sort of this interactional view of context, which says that the context is defined dynamically as part of the interaction. Context sort of gives rise to the activity and the activity changes the context. So there's kind of a mutually reinforcing relationship between those two. And, again, that independent does not necessarily observe some sort of variables related to that. So the context can be represented as an explicit numerous sort of static attributes. This is the representational view. Again, things like time, and location, or mood, or device, for example. Things that have been typical, typically used in context the word, computing. In ubiquitous computer or mobile computing. An application, of course, there, is that the relevant contextual factors or variables, and their structure, have to be known at design time, right? So you have to say, okay, yes, you have items and you have ratings, users have general preferences. But in addition to that, we have some kind of contextual variables, like time and location. And other things that define the context and we know what these factors are before? So that's how some designing implications for systems, obviously. And of course the information about the instantiation of these variables have to then be acquired by the system, right? So if location is one of your contextual variables and your system needs to be aware of it's location, right? Like the GPS for example, or some sensor devices that find that. Something about time or something about let's say, there are some other aspects of contexts that may have to be deciphered, or extracted in other ways, possibly explicitly by the user. So, when we're talking about the representational context, then the context might look something, a user profile might look something like this, right? So we are typically used to in the traditional recommender system domain seeing a user rating matrix which has let's say, users as rows and items as columns, and then you got the ratings associated with those items. So you can think of that as a cross product of users and item, being mapped to space of ratings, in some scale. And those ratings could be explicit ratings, or implicit, preferences, etc. In a context-aware situation, then you have these other, at least in the representational framework, you have these other attributes or factors. Things like time, location, companion, lets say etc, as you can see in here. And the same user might in fact have shown different preferences for the same item across these different contexts. So, if you look at this table for instance, it gives you an idea what type of data we are dealing with, when dealing with representational context. You have data that involves user preferences, but those user preferences also include information related to a whole set of different contextual, pre-specified contextual variables like time, location, companion. And what you also see in here is that when we talk about context in this sense, in this framework, you are talking about some specific combination of these variables, right? So one context would be seeing the movie Titanic on a weekend in a theatre with your family, right? That combination is one context, each one of those individual variables are some contextual variable or factor associated with it. So, In the representational domain, I should say by the way that most of the work in context of a recommendation has focus in this representational framework. So people assumed that you already have some pre-specified set of contextual variables again, like time and location etc. And most of the systems that people have built or algorithms people have came up with, then have followed three different general architectures, if you will. One is contextual prefiltering, the idea there is that you have the data, the kind of data that we were just looking at. But then you sort of filter that data. So if you want recommendations that are relevant to a particular context, you filter only those ratings that are relevant to that particular context. So once you've done that, then contextualized data is just then users' items and ratings. But it's a subset of all of their ratings, right, you're focused on a particular context. Then at that point, you just use a standard recommendation algorithm, right? Like k-nearest neighbor, matrix factorization, etc. And you give recommendations. The fact that those recommendations are context aware is simply because you have filtered out all of the other contexts, right? So that's kind of an idea of contextual prefiltering. And part of the drawback in that kind of approach is, of course, often in recommender systems, we are dealing with datasets that are extremely sparse. So this even makes thing more sparse, right? >> Yeah. >> So that could potentially be a problem. Postfiltering is sort of the other side of the spectrum. So the idea there is that you have datasets with a different context, initially you ignore the context. You sort of build your recommender system and then you give recommendations. But your recommendations are the ones that are post filtered based on relevance to a particular context. And there are different ways of trying to figure out what that relevance is? This is a typical approach that a search personal organization uses, right? So you sort of personalize a search, you take into account relevance, and you give a set of items back based on a user's query. But then afterwards, you re-rank things based on similarity, let's say, to some profile of the user. A more systematic approach is to actually incorporate context in the learning process, right? So when you build your models, when you're trying to learn the models that allow you to make predictions, you actually take into accounts the context itself as part of the learning process. And this something's called contextual modeling. And I guess I would say the bulk of the work that's been done in recent years is really in this, kind of the last column there. So let me just circle back and just briefly also talk a little bit more about interactional view. So the difference here is that we don't necessarily know, we don't observe some set of variables that tell us what context it is? We don't necessarily know the location, we don't necessarily know the time, space, etc. And, in fact, there are some domains where those are not necessarily the relevant things, but context, maybe it is a task that you are performing, right? And the way context is then understood is as part of your interaction. If I am, let's say, navigating a particular website to perform a particular task and then I'm done with the task. And I move on, and start navigating another part of the website, to perform a different task. Those really should be seen as two different contexts, in a sense, right? And the only thing that tells me that this context has changed, or that there's a difference between those two, is user's interaction itself and possibly the interaction of other users in the same environment in the past, right? So in that sense context is not observed but it could be inferred, possibly kind of as a stochastic process with a bunch of states and those states representing different contextual conditions. So, this has its own set of challenges, right? On the one hand, the benefit is that you don't necessarily need to have some pre-specified set of contextual variables And therefore, if later on after you've designed your system, other variables come into play. You don't have to change the design of your system, it's more dynamic. On the other hand, you never actually cannot point directly to some set of conditions that says, okay. This set of variables with these values are the current context. You have to sort of or be able to infer this. So these of course lead to the different types of systems and different understandings of what context awareness means. Generally, you can also think about characterizing the environment for context-aware recommendation. And how context itself changes, right? So it's representational framework I was telling you about, you could think about a situation, the context is fully observable, so everything is known about the context. And the context itself is static, and doesn't change. You could also have partial observe, partially observable context. You could unobservable context, the kind of the situation was just describing, where there is latent knowledge of context that you infer, but you don't observe it directly. And furthermore, the context can either be static or dynamic, right? So it's good change or it could always be the same sort of either latent factors or some specific observable factors that describe conflicts. So it can also kind of the idea of conflicts and how it factors into the design of systems among these two dimensions, that tell you a little bit about what you can do about that? So, There will be a lot of work on different kinds of contextual recommendation algorithms. Some extensions of standard collaborative filtering where some of the earlier models things like pre-filtering approaches that we described based on user and item splitting, the idea that you would sort of treat a user, for example, as multiple users, given that they may have rated the same item in different contexts, right? So their rating different contexts would sort of be treated as different users. And then you apply the standard two dimensional kind of recommendation algorithms events. Same thing with item splitting, so the other dimension. Differential context modeling is an approach where you try to identify some subset of the most relevent contextual variables and then maybe associate different weights with them, as part of the model process. There's some heuristic approaches, they're the distance based, and most of these of course, if you know what context variables you're dealing with, would require also similarity or distance matrix specifically for those dimensions, right? So how do you compare, for example the order time dimension versus the location dimension versus than lets say mood dimension. Some can be more complex than others that you would have to define specific similarity or distance metrics for each one of those approaches. The more recent approaches are primarily based on matrix or extensive factorization. The idea there for example is, to be able to destroy rather than looking at the user item matrix, you look at a more general tenser which include context sort as other dimension. And then you do factorization of this tenser and then use those factorized matrices to make predictions kind of like you do in a standard matrix factorization, except you've got an extra dimension for each of the contexts that you want to model. There are other extensions of context where matrix factorization. There's also some work on factorization machines which allows you to explosively sort of represents different contexts and dimensions kind of in the same key dimensional space, right? So we can think about users and items that's being two dimensions and the different contexts could be all different additional dimensions in the same table, and then you sort of build a factorized model based on that. Most of these that I named up to this point are sort of assumed this representational framework, right? Where you know what these contextual variables are. When you get to the interactional framework then you're dealing often with probabilistic latent variable models, because you have to sort of infer context by finding out for example what those, trying to look at data from let's say large number of users or possibly some external data. Let say by social media data, etc. Or some content features associated with items. And then be able to extract latent variables that might describe different kinds of contexts that users are interacting with. If the context is not completely dynamic, then you may have to use active learning approaches based on exploration, exploitation. And in fact, we have done some recent work in this particular area where the system sort of learns as it goes. And the idea of active learning and standard reinforcement of learning has been around for a long time. The new bit in here of course is to try to get the system to understand when there is a change in context. So, the system layers and layers and layers of data, has a model that sort of is learned over time. But then it may have to retract a portion of that if it notices a change in context, because some of the things that were learned are not relevant any more.
