Equipping your e-commerce site with even the most basic recommender system delivers tremendous business benefits. You find your average order value shoots up because your customers add your suggested products to their carts. Your site's customer lifetime values and customer retention go up because your customers perceive that you somehow sense what they want. Click-through rates to your product ads and conversions from your marketing emails rise. You can upsell and cross-sell products to your customers based on their interests.
All of that comes from using just basic recommender systems, the ones that treat users and products as nameless cohorts. Now imagine just how much more beneficial hyper-personalized recommendations can be — all carefully selected according to every customer's individual desires. Context-aware recommender systems (CARS) hyper-personalize product suggestions by sensing and understanding all the social, financial, emotional, and environmental factors — collectively called the “context” — that influence your customers to make their purchase decisions.
Even inconspicuous metrics like how long your customer kept a product page open, how they moved their mouse over the page, and how quickly they scrolled adds to the context.
With a context-aware recommender system, you can not only market and sell to your customers at the right place and right time, but also plan ways to recreate some of the contextual conditions that may persuade them to buy more from you.
A CARS brings context awareness and hyper-personalization into every step of your customer's shopping experience. Amazon was already getting as much as 35% of all sales through recommendations back in 2013 and the proportion is likely to be higher now.
You’d have seen Amazon’s persuasive on-site techniques for selling more — recently viewed items, “frequently bought together” sections, and “customers who bought this also bought” sections. They even serve you category recommendations through their “related to items viewed” and “best sellers in category” sections. Wherever you look in Amazon, you find recommendations in action. A CARS gives your site the same powers.
The products shown on your customer’s front page are selected based not just on what they bought in the past or what's sitting in their cart but also on factors like the time of the day, the device they’re using, the environment they're in, or their current activity. You can provide your customers with personalized “top picks for you” pages like Amazon does.
When they search for a particular product, a CARS uses their search query to fine-tune the list of product suggestions in real-time. It can even infer context based on the patterns in their typing. Did they seem unsure when typing a word? That may act as a hint to broaden the categories of products. Did they modify a search multiple times in a row? That may mean none of the top results were relevant to them till they finally decided to click a product.
A CARS can learn that one of your customers prefers using your app on weekdays and your website on weekends. Context awareness can help you understand such inconspicuous customer behavior and tune product suggestions suitably.
You can send hyper-personalized suggestions through marketing emails customized to every one of your customers. Contextual factors like the month and the day of the week influence exactly what your customer wants at the time and thus influence your recommendations too. You can notify your customers about an upgrade or newer version of a product they bought in the past. You can tell them about the best selling products by brand based on their category interests just like Amazon does. Personalized marketing emails are a great way to upsell products and increase customer lifetime values.
A CARS can tune its suggestions based on what's sitting in your customer's cart and for how long. It can use socio-economic or environmental clues to make frequently-bought-together and other-customers-bought suggestions a lot more fine-grained. Two customers are no longer similar simply because they bought the same item but because they bought the same item in the same hour on a particular day of the month from the same city.
Before diving into CARS, it helps to understand the idea of context better. Context covers all the social, financial, psychological, and environmental factors that influence an interaction, such as a customer purchase or a product rating.
Such contextual information can be very complex and often domain-specific, covering diverse factors like:
A CARS uses knowledge discovery to uncover the semantic relationships between users, items, and context that influence a user’s interactions. Context awareness keeps your recommender systems adaptive to changes in the user’s environment.
In 2010, Gediminas Adomavicius and Alexander Tuzhilin proposed three paradigms for context awareness in their paper on context-aware recommender systems — pre-filtering, post-filtering, and context modeling. The two filtering approaches inject context awareness either before or after a regular recommender system has given results. But because they’re not ideal recommendation approaches, we’ll focus only on context modeling.
In contextual modeling, the core recommendation algorithm — like collaborative filtering — is modified for context awareness using a multidimensional approach, by adding contextual features to the normally two-dimensional set of user versus item data. We’ll explore these algorithms in detail next.
It helps to first understand the motivation behind using deep neural networks (DNNs) for recommender systems before exploring specific models.
Recommender systems use different approaches like collaborative filtering that recommends based on user similarities, or content-based filtering that recommends based on item similarities. Hybrid recommender systems combine multiple approaches.
An algorithm common to these recommendation techniques is matrix factorization (MF). The data you have is a user-item matrix derived from interactions captured in your business databases. Given such a matrix, you want to derive the per-user and per-item weights that can produce that matrix. One of the easiest ways to do this is matrix factorization.
Once you know those per-user and per-item weights, you can determine both user similarities and item similarities using clustering or Bayesian networks, and predict the rating that a new user is likely to give to an item.
However, MF’s simplicity also limits its predictive capability. Matrix factorization is a simple linear operation that’s suited to legacy hardware. But state-of-the-art DNNs running on modern hardware can model the complex real-world relationships between users and items much better using non-linearities.
In 2019, Livne et al. proposed context awareness using deep neural networks to learn the complex relationships between users, items, and contextual factors. They used DNNs like long short-term memory (LSTM) and autoencoders for latent representation learning of the context.
The extracted latent context vectors were then integrated into the Neural Collaborative Filtering (NCF) architecture that used a DNN for collaborative filtering using more expressive non-linear functions in place of linear matrix factorization.
Let’s understand what latent context modeling means and how these models advanced the state of the art in CARS.
As you saw earlier, context can be complex, multi-faceted, and domain-specific. While your domain experts can guess some obvious and logical factors, the real world can be far more complex than they ever imagined. Can a music expert predict the influence of local weather, local news, or political beliefs on your customer’s music choices? These are the kind of real-world latent influences that deep neural networks can discover and model.
Because you don’t know all the factors that influence your customer’s decisions, the best approach is to avoid manual feature selection altogether. Just throw in every measurable factor you can think of — social, financial, political, environmental, social media sentiments, location, weather, local news, or smartphone sensor readings. Your deep neural networks can then figure out their individual and combined influences on your customers’ choices.
But such a hodgepodge of diverse features can easily number in the thousands. Even on modern hardware, this increased dimensionality and sparsity can cripple most recommender algorithms in production environments.
Latent context modeling improves both dimensionality and sparsity by using shorter vectors to express the same information. The next logical question is: Which neural network is good at this kind of information compression? The study compared context-aware recommender systems that used autoencoders and LSTMs for this task.
An autoencoder is a DNN whose expected output vector should have the same values and length as its input vector. But having significantly fewer neurons in its hidden layers forces it to learn to express the same input information using shorter vectors. You can use these shorter vectors instead of the high-dimensional input data because they contain the same information and can also be processed efficiently. Such an approach is an example of representation learning for dimensionality reduction.
When used for latent context modeling, the autoencoder reduces the high-dimensional raw context features to shorter vectors. However, it doesn’t treat context as a first-class dynamic entity whose long-term changes and influences should be accurately modeled. Instead, it looks at just the current context and treats it like any other information that should be compressed. You can think of it as more of an optimization approach. The paper calls this model the Latent Neural Context Model (LNCM).
These latent context vectors are integrated into the NCF model by combining them with user and item vectors passed to its first multi-layer perceptron (MLP) layer.
An LSTM is good at predicting sequences because it assumes the current value of a feature depends on all its previous values. Real-world phenomena like smartphone sensor readings and weather changes work exactly like that.
The paper proposes an LSTM as an alternative to an autoencoder and calls it the Sequential Latent Context Model (SLCM). It’s an LSTM encoder-decoder sequence-to-sequence (or seq2seq) model that predicts the next context values based on their previous values. Once trained to predict the context accurately, you can use the encoder block’s final outputs as reliable context vectors that contain all the same information as the raw context data but in a compressed efficient form. Moreover, these vectors will reflect the true nature of context better than the ones from an autoencoder.
These sequential latent context vectors are combined with user and item vectors passed to the NCF model’s first MLP layer.
The study compared recommender systems on two datasets — CARS and Yelp.
The CARS dataset is a private dataset with about 21,397 user ratings for points of interest like restaurants and cafés. It contains as many as 247 contextual features collected from smartphone and other data services and grouped into the following contextual groups:
The Yelp dataset is a public dataset of points of interest published by Yelp. About half a dozen contextual features are derived from the raw data — year, month, day of the week, city, holiday or weekend indicators, season, location, and more.
The study proposed three context-aware DNN models and compared them against four baseline models. The three context-aware DNN models were:
The four baseline models were a mix of traditional and neural collaborative filtering models with some of the traditional ones being context aware:
These seven models were compared on these metrics:
The results show all three context-aware DNN models outperforming the four baseline models on all metrics. Among them, the sequential latent context model has impressive top-k hits along with low error numbers. The study establishes the effectiveness of DNN context-aware recommender systems.
In 2020, the same team of Livne et al. proposed better context modeling approaches to improve explainability, privacy, trust, and data collection efficiency — the kind of improvements that can elevate your customers’ opinions about your business.
While latent context modeling improves the dimensionality of data and the quality of recommendations, it has these disadvantages:
To address all these, we need to improve both context modeling and data engineering.
Genetic algorithms (GA) are a neat solution to overcome all these disadvantages because they reduce dimensionality while retaining explainability and efficiency.
In computer science, a genetic algorithm is an optimization search method. It iteratively looks for the most optimized option in a set of possible solutions, using biology-inspired concepts like mutation, fitness, and crossover.
In context-aware recommendation methods, the context features are selected using a genetic algorithm as follows:
The authors ingeniously addressed this intractability problem using a second neural network that we’ll understand in the next section.
But why do all this? Because it has several benefits:
The paper uses three different machine learning and deep learning models:
The study evaluated this approach on two datasets — CARS and Hearo. Both are private datasets with ratings for various points of interest (POIs). The CARS dataset is the same one described previously but used as many as 480 contextual features this time.
The Hearo dataset provides recommendations of POIs and users’ binary ratings on the provided recommendations. The ratings are from 77 users and have 661 contextual features.
The study compared this GA approach against nine other models, including the three latent context DNN models we studied earlier.
They used two metrics for evaluation — area under ROC curve (AUC) and log loss. On both, the GA approach outperformed all the other models.
Context awareness has been a game-changer for e-commerce recommender systems, thanks to its uncanny ability to provide highly personalized recommendations by understanding your customers’ minds. Given that transformer networks are raising the bar in other information systems, we can anticipate that future research directions are headed that way in e-commerce recommender systems too.
An e-commerce CARS of the future is likely to be even more context-aware. Family members of your customer and their interests can influence your product recommendations as much as they influence your customer’s purchase decisions.
Data from your customer’s banks or credit card companies — all acquired with their permission — can help your CARS tailor its product suggestions to your customer’s current financial situation and price sensitivity. The possibilities are limitless.
Contact us to equip your marketing and sales departments with context-aware recommender systems that personalize your products and services for each of your customers.