Industry Session 1: Novel Uses
Date: Monday, Sept 16, 2019, 14:00-15:30
Location: Auditorium
Chair: Tao Wang
Using AI to Build Communities Around Interests on LinkedIn
by Abdulla Al-Qawasmeh and Ankan Saha (LinkedIn)
At LinkedIn, our mission is to connect the world’s professionals to make them more productive and successful. Our team, Communities Artificial Intelligence (AI), at LinkedIn helps our members achieve this goal is by providing a platform where communities can form around common interests and shared experiences. Fostering active communities at LinkedIn can be broken down into the following components:
- Discover: Help members find new entities (members, companies, hashtags, and more) to follow that will expose them to communities that share their interests.
- Engage: Engage members in the conversations taking place in their communities by recommending content from their areas of interest.
- Contribute: Help members effectively engage with the right communities when they create or share content.
These three components form the main pillars of a content-driven ecosystem and our goal is to use AI to successfully close the loop between Discover (via providing relevant follow recommendations), Engage (via delivering engaging content to users from their areas of interest), and Contribute (via suggesting hashtags to content creators to target the right audience). A diverse set of AI techniques is required to address the challenges that arise in each of these components. These techniques include: Supervised Learning (XGBoost, Logistic Regression, Linear Regression), Wide and Deep Models, Natural Language Processing (e.g., Word Embeddings, ngram matching), and Unsupervised Learning. In this presentation, we will provide an overview of the AI techniques we use to form active communities on LinkedIn. We will describe two solutions in detail. First, we will describe how we have built our Follow Recommendations product. The goal of the Follow Recommendations product is to recommend entities to a member that the member finds both immediately relevant (i.e., increase the probability the member will follow the recommended entity) as well as engaging in the long run (i.e., the recommended entity produces content that the member finds relevant). Our analysis of the performance of our follow recommendations models has shown the superiority of nonlinear models compared to their linear counterparts. To manage the explosion of data emanating from terabytes of features generated from (viewer, entity) pairs, we use an innovative 2-D hash join algorithm that was developed at LinkedIn. We are also moving towards a hybrid scoring architecture. This allows us to score candidates with complex offline models and then re-rank these candidates based on more time-sensitive contextual features online. This generates more relevant and timely recommendations for the members based on their recent activity on different parts of the LinkedIn ecosystem. Second, we will describe our approach to solve the problem of Hashtag Suggestion and Typeahead. Hashtags are a great tool that allows members to expand the reach of their posts to the right audience (or communities). Our Hashtag Suggestion and Typeahead (HST) product was built to aid members in adding hashtags to their posts. We do not only recommend hashtags that the member is likely to select into their post, but also hashtags that are more likely to get the member the most online feedback. We call the latter aspect downstream utility (or engagement). However, before realizing this utility, the member has to actually select from the recommended hashtags. Therefore, the HST product is produced by combining two models. The first model maximizes the probability that the member will select the suggested hashtag and the second one optimizes for downstream utility. Based on content consumption behavior on LinkedIn, we have a good understanding of the supply and demand of content tagged with a specific hashtag. This information enables us to shape the inventory as well as traffic in individual hashtag domains, thus providing a better experience to content-starved communities.
The Trinity of Luxury Fashion Recommendations: Data, Experts and Experimentation
by Ana Rita Magalhães (FarFetch)
Farfetch is the leading platform for online luxury fashion shopping. We have more than 3000 brands and high-end designers with the biggest catalog of luxury products available worldwide to more than 1 million customers. The high-end luxury fashion segment where Farfetch operates in is a notably complex and intricate field. Fashion trends change very fast and can come from anywhere, at any time, thus being very hard to capture. Ultimately, people’s tastes are very personal and hard to extrapolate. Users of luxury websites have understandably high expectations and demand a high-end, curated and knowledgeable experience in all aspects. To achieve this, the recommendations engine powering the Farfetch platform is being built on top of three main pillars: 1) data, 2) expert knowledge, and 3) experimentation. Data is obviously the core of any automated recommender system. Like many e-commerce platforms, we collect and leverage various implicit interactions by tracking our users’ journeys on Farfetch.com and apps, as well as the explicit preferences they often set – such as their favourite designers. From implicit feedback data we started building the state-of-the-art recommender systems based on collaborative approaches only to realize that our catalogue would not allow for item-item collaborative recommenders, since a product’s lifetime is either too short with unique pieces being bought as soon as they go live, or too long with some timeless iconic items lasting forever. Hence, we needed to implement hybrid versions of collaborative-based recommenders which emphasized the products’ content data. Throughout the experimentation process over these algorithms, both implicit and explicit feedback data seemed to fall short to encode the sense of fashion expected by our customers. The obvious next step was to use the internal knowledge embedded in several teams of fashion experts and stylists. Although not trivial, there are many ways we can leverage this expert knowledge into improving the fashion understanding of our recommender systems:
- Our content editors create the editorial pages with the latest trends and write the products’ descriptions. This data allows us to build the relationships between designers to create adjacency models and incorporate taxonomy data employing NLP approaches.
- Our visual merchandising experts curate crucial listing pages with products respecting business rules, fashion trends and our signature on fashion. This allows us to encode colorflow and style trends by using style transfer techniques such as computing Gram matrices from convolutional feature maps.
- Our stylists manually curate outfits respecting Farfetch’s style identity. This allows us to build automated outfits based on siamese neural networks on top of Convolutional Neural Networks.
In order to tie these sources of information together in a seamless manner, we follow a strict experimentation workflow, where we iterate fast, deliver in a controlled way through AB testing, and track and evaluate the impact in different dimensions. This process has allowed us to optimize the business value of the system in different contexts and gain a better understanding of our customers and what works and doesn’t work for them. In this talk, we will share the Farfetched solutions of our journey on building personalized recommendations in the segment of luxury fashion using data, experts and experimentation.
Just Play Something Awesome: The Personalization Powering Voice Interactions at Pandora
by Vito Ostuni (Pandora)
The adoption of voice-enabled devices has seen an explosive growth in the last few years and music consumption is among the most popular use cases. Music personalization and recommendation plays a major role at Pandora in providing a delightful listening experience for millions of users daily. In turn, providing the same perfectly tailored listening experience through these novel voice interfaces brings new interesting challenges and exciting opportunities. In this talk we will describe how we apply personalization and recommendation techniques in three common voice scenarios which can be defined in terms of request types: known-item, thematic and broad open-ended. Known-item search requests are the most common scenario where users have a well defined and clear intent which is looking for a specific item in the catalog or their personal collection. A voice interface makes the task natural and easy to accomplish since the user is not required to type on a small keyboard. Solving for this specific task involves performing an entity search against a large music catalog and personal user collection. This can be very challenging due to imperfect voice utterance transcriptions, unconventional entity names and the numerous combinations of ways a user can ask for music entities. We employ personalization algorithms for entity disambiguation which can be caused by the presence of homonyms, homographs and homophones terms in the catalog.
Another common voice use case is to ask for music regarding a specific theme or context such as a genre, an activity, a mood, an occasion or any combination of those. This scenario differs sharply from the known-item case in that multiple results might, based on user varying contexts, be relevant rather than a single clearly relevant one. For example, a rap music fan would not enjoy a country workout playlist when asking for ‘music for working out’ but may like a hip hop one. This problem can be quite complex to solve as it involves different areas such as voice spoken language understanding, content tagging and personalization. We will describe how we use deep learning slot filling techniques and query classification to interpret the user intent and identify the main concepts in the query. After that, we will discuss some of the content tagging work we have done to classify music according to these voice specific themes. Lastly, we will touch upon how we use recommendation techniques to deliver personalized and unique results to each individual and describe the challenge of balancing the delicate trade-off between query relevance and personalization. The third category of voice queries we will describe are broad or open-ended requests. Voice users often skip the hard work of thinking about what they actually want to hear and command: ‘just play something awesome’. A music service should still meet these expectations instead of interpreting those commands as literal requests. We discuss exploit and explore trade-offs made in the recommendation item pool generation process. Here the exploit pool contains items aimed at re-consumption, while the explore pool contains new items with specific context match.
Finally, we will discuss differences and challenges regarding evaluation of voice powered recommendation systems. The first key difference is that in the standard recommendation system settings evaluations are based on UI signals such as impressions and clicks or other explicit forms of feedback. Since pure voice interfaces do not contain visual UI elements, relevance labels need to be inferred through implicit actions such as play time, query reformulations or other types of session level information. Another difference is that while the typical recommendation task corresponds to recommending a ranked list of items, a voice play request translates into a single item play action. Thus, some considerations about closed feedback loops need to be made. In summary, improving the quality of voice interactions in music services is a relatively new challenge and many exciting opportunities for breakthroughs still remain. There are many new aspects of recommendation system interfaces to address to bring a delightful and effortless experience for voice users. We will share a few open challenges to solve for the future.
Future of In-Vehicle Recommendation Systems
by Juergen Luettin, Susanne Rothermel and Mark Andrew (Bosch)
Future in-vehicle recommendation systems will assist the driver or passenger in all situations before, along, and after a trip. Based on preferences and needs of the user and by taking the current situation and available context information into account, they will provide the right recommendation at the right time. Bosch is the world’s largest automotive supplier, delivering a full range of products and services from power-train, infotainment, HMI, connected mobility, driver assistance to automated driving. This talk will present challenges, concepts and recent technical progress in in-vehicle recommendation systems developed at Bosch including details of a combined routing, charging, and point-of-interest(POI) recommendation system. There has been tremendous progress in the field of location-independent recommendation systems, such as recommending films, music, news or shopping articles. The ubiquity of user location information, provided by connected devices, has paved the way for location-based services (LBS), and their combination with social networks have extended these to location-based social network (LBSN) services. In-vehicle recommendation systems go a step further by extending LBSN services with vehicle context and vehicle specific applications. This can support the user in various applications, such as routing (e.g. route and point of interest recommendation), infotainment(e.g. music or news recommendation), communication (finding a contact, fast call) and in-vehicle control (e.g. seat position,ambient light or HVAC settings). Out-of-vehicle assistance includes the control of connected devices in smart buildings such as alarm systems, heating, kitchen and entertainment devices. We present an important application of in-vehicle recommending systems, a combined routing, charging and POI recommender developed at Bosch. Routing and charging optimization for electric vehicles was described for optimizing the shortest feasible path,optimizing constrained shortest path, optimizing charging grid demand and opportunities, and optimizing minimum cost. These approaches focus on single criteria based optimization. We describe the first system with combined route optimization,charging station search and POI recommendation. It optimizes three criteria: finding the optimal route with the optimal charging stations, so that the vehicle always has enough energy, and finding the optimal POIs along the route, where ‘optimal’ depends on the drivers preferences and rich context information covering user,vehicle and environment.
Material
Designer-Driven Add-to-Cart Algorithms
by Sandhya Sachidanandan (IKEA)
Although real-time dynamic recommender systems have been applied successfully by e-commerce and technology companies for more than a decade, we at IKEA Group have just started our journey into this exciting field. At IKEA, customer experience is at our heart, and a key principle for any machine learning algorithm that we design to improve this experience is that it should act as an extension to the home-furnishing expertise that our co-workers have developed and fine-tuned for more than 75 years. In this talk, we discuss a particular recommendation strategy that projects the inspirational shopping experience of our blue boxes onto our digital touch points by defining a notion of style from our vast collection of inspirational content. To go beyond classical, transaction-based collaborative filtering strategies, we take as our starting point the different types of images taken of each product when launched. Our current implementation relies on the following 3 types of images:
- white-canvas, referring to an image of a product displayed on a plain white background;
- context-based, which shows a product in the larger context of a room, but where emphasis remains on the product itself;
- inspirational, in which a product is shown in a purposefully atmospheric setting with focus on the entirety.
By extracting the product range displayed in our tagged inspirational images, we initially construct a graph of products that embeds the mindset of our talented designers. Add-to-cart recommendations are then generated from the resulting graph based on user-behaviour data collected from our digital touch points (app, web) and transactional data from purchases made online, or in one of our IKEA stores. To implement the strategy, we have come across a few interesting (stand-alone) problems along the way; notably, we faced a severe lack of properly tagged inspirational images, and much of our furniture today does not appear in our inspirational collection. To circumvent the latter observation, we pursue a supervised learning approach that automatically identifies products that 1) complement each other with regards to function, and 2) match in terms of style. We do this by taking product metadata attributes and the full collection of product images as input. We also discuss how we use a combination of features extracted from context-based and inspirational images using a pre-trained ImageNet model, together with manually tagged inspirational images and transaction data from stores to create our training data. The use of both context-based and inspirational images distinguishes us from similar methodologies in the fashion industry and enables us to capture the notion of complementary products in a satisfying way.
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