Amazon discovered early on the value of community-defined content (this is, in fact, still its true -- and largely unrecognized -- contribution, not "recommendations"). A/B presentation and optimization services have cracked open the window onto visitor and community participation in terms of presentation, albeit indirectly. iGoogle, Facebook, et al took the next step and allowed visitors to define various aspects of personal and public content and presentation.

Even more significant, few sites today are constructed solely from internal site resources. Hosted metrics, recommendations, news, store locators, stock tickers, friend followers, and so on and so on are rapidly deconstructing the whole notion of "place" through the active participation of the "web-fabric" layer of the web community.

From this perspective, most recommendation services are still stuck in the feudal worldview: the black box recommender knows you (whether "you" are a visitor or place-owner) better than you know yourself and determines, in its infinite wisdom and authority, what content should be presented to you. The place-owner may have some input into presentation and even, though less so, content, but only in a very limited way.

While this situation is useful in certain cases because of the total passivity afforded the place-owner and visitor, it severely limits the potential contribution of recommendation technology.

Personal, Real-Time Conversation

There is a broader view of recommenders, though. The business value of recommendations is that they bring the place-owner into a one-on-one, real-time, conversation with the visitor. As such, a recommender must be able to accommodate the active participation of both the place-owner and visitor. Recommenders play the role of the salesperson, the agent in the company who has one-on-one contact with each shopper. This is in contrast to the site designer, who is more akin to the display designer in a bricks-and-mortar store and who can only target segments of the population who are expects to pass the display, not individual shoppers. Recommendations are also narrower in concept than personalization tools, which are analogous to store greeters: they may personally greet you when you arrive, but they generally don't follow you through the store as you shop or interact with you in real time.

Okay, but why a conversation? Consider the typical interaction between a sales agent and shopper in a bricks-and-mortar store. The shopper enters the store and starts looking around. At some point, the sales agent asks, "Can I help you?" "No thanks, I'm just browsing," By this point, the sales agent has probably already observed the shopper and made some inferences about the shopper's intentions and receptivity and about associated sales opportunities. The shopper, in turn, has been assessing the store's inventory and pricing.

Like these sales agent, place-owners have a tremendous amount of knowledge about shoppers, sales tactics (like cross-selling, upselling), and their own business objectives, both short- and long-term. Much of this knowledge is unavailable to automated recommendation engines, no matter how much data they gather (and the ultimate prize for optimizing discounted infinite-horizon shopper value is computationally intractable even if we had the data). So, the recommender is better tasked to take advantage of the wisdom of the place-owner "in the moment." Of course, an uninformed recommender is just a degenerate case and may still be useful.

One advantage of the web is that transaction costs are low. Most place-owners can't afford to have human representatives in sessions. Most explicit communication by the place-owner must be in the form of policy or strategy, rather than actual real-time communication. (Notwithstanding this, interaction with a live sales agent may well be an appropriate option for a recommender to trigger in certain situations.)

Situation/Response

One way to think about this is like "situation/response". The situation description might cover visitor location, web page visited, catalog, date (e.g. if it is a holiday), place-owner internal item information (e.g. from a supplier catalog or internal access and sales statistics), visitor community information (e.g. sales ranking, review ranking), or even external information (e.g. Google search ranking, Amazon ranking). The response should be a specification over recommender behavior, as well as resulting recommendation content (e.g. show a pair of Nike's under $50), and presentation, both style and modality (e.g. use an animated GIF showing all available colors). Perhaps, as mentioned above, modalities even extend to bringing a live sales agent into the real-time conversation.

While limited work has been done on place-owner participation in recommendation-system content and presentation, the situation is far more dismal for the visitor. A broad array of modalities are available for visitor interaction, but few if any are available in most recommendation systems. A simple "No, that's not what I'm looking for" (e.g. a thumbs-up or thumbs-down icon on a recommendation thumbnail) might go a long way to making the shopper feel noticed and appreciated. I can say to a human store clerk, "I'm looking for a pair of Nike's under $50" -- why can't I tell the average recommendation system the same thing? Notice that this starts to overlap with the expressivity needed on the place-owner side. The main difference is that the visitor is always in the moment, so there is (usually) no need to specify context.

The above sketch is intended to crack open the door on the enormous range of possible capabilities, modes, and time-scales of participation by place-owner and visitor. Once we've opened this door, there is no reason not to open it to the visitor community and the web-fabric community as well. There are three primary points:

1. A place is no longer a feudal domain; all stakeholders now demand a voice.
2. A recommendation engine is the locus where understanding of content and understanding of the visitor-in-the-moment come together.
3. As a result, recommendations are the logical ground for crucial real-time conversations between place-owner and visitor.

Given our initial definition of place, we might also ask about the role of and opportunity for participation among other stakeholders. For example, can the interaction between the site designer and the visitor or web-fabric community also be viewed as an ongoing conversation, rather than an episodic, one-way information flow at the time of site design? The answer is yes, but that is a topic for another time.

Conclusion

Recommenders need to open up to allow increased place-owner, visitor, and community participation in both content and presentation. This is best done with the assumption that a recommender is meant to facilitate situated, in-the-moment conversation between the place-owner and visitor.

This was a guest post by Bruce D'Ambrosio, VP and Chief Architect, OnDemand Personalization at ATG, Inc. He was the founder of CleverSet, which was acquired by ATG. He is also a former Oregon State University computer science professor.